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Where technology & law meet: Open Access research on data security & its regulation ...

Strathprints makes available Open Access scholarly outputs exploring both the technical aspects of computer security, but also the regulation of existing or emerging technologies. A research specialism of the Department of Computer & Information Sciences (CIS) is computer security. Researchers explore issues surrounding web intrusion detection techniques, malware characteristics, textual steganography and trusted systems. Digital forensics and cyber crime are also a focus.

Meanwhile, the School of Law and its Centre for Internet Law & Policy undertake studies on Internet governance. An important component of this work is consideration of privacy and data protection questions and the increasing focus on cybercrime and 'cyberterrorism'.

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A low-complexity energy disaggregation method : performance and robustness

Altrabalsi, Hana and Liao, Jing and Stankovic, Lina and Stankovic, Vladimir (2014) A low-complexity energy disaggregation method : performance and robustness. In: 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG). IEEE, Piscataway, NJ., pp. 1-8.

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Abstract

Disaggregating total household's energy data down to individual appliances via non-intrusive appliance load monitoring (NALM) has generated renewed interest with ongoing or planned large-scale smart meter deployments worldwide. Of special interest are NALM algorithms that are of low complexity and operate in near real time, supporting emerging applications such as in-home displays, remote appliance scheduling and home automation, and use low sampling rates data from commercial smart meters. NALM methods, based on Hidden Markov Model (HMM) and its variations, have become the state of the art due to their high performance, but suffer from high computational cost. In this paper, we develop an alternative approach based on support vector machine (SVM) and k-means, where k-means is used to reduce the SVM training set size by identifying only the representative subset of the original dataset for the SVM training. The resulting scheme outperforms individual k-means and SVM classifiers and shows competitive performance to the state-of-the-art HMM-based NALM method with up to 45 times lower execution time (including training and testing).