Efficient Database Generation for Data-driven Security Assessment of Power Systems

Power system security assessment methods require large datasets of operating points to train or test their performance. As historical data often contain limited number of abnormal situations, simulation data are necessary to accurately determine the security boundary. Generating such a database is an extremely demanding task, which becomes intractable even for small system sizes. This paper proposes a modular and highly scalable algorithm for computationally efficient database generation. Using convex relaxation techniques and complex network theory, we discard large infeasible regions and drastically reduce the search space. We explore the remaining space by a highly parallelizable algorithm and substantially decrease computation time. Our method accommodates numerous definitions of power system security. Here we focus on the combination of N-k security and small-signal stability. Demonstrating our algorithm on IEEE 14-bus and NESTA 162-bus systems, we show how it outperforms existing approaches requiring less than 10% of the time other methods require.Comment: Database publicly available at: https://github.com/johnnyDEDK/OPs_Nesta162Bus - Paper accepted for publication at IEEE Transactions on Power System

A System for Induction of Oblique Decision Trees

This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.Comment: See http://www.jair.org/ for an online appendix and other files accompanying this articl

S-TREE: Self-Organizing Trees for Data Clustering and Online Vector Quantization

This paper introduces S-TREE (Self-Organizing Tree), a family of models that use unsupervised learning to construct hierarchical representations of data and online tree-structured vector quantizers. The S-TREE1 model, which features a new tree-building algorithm, can be implemented with various cost functions. An alternative implementation, S-TREE2, which uses a new double-path search procedure, is also developed. S-TREE2 implements an online procedure that approximates an optimal (unstructured) clustering solution while imposing a tree-structure constraint. The performance of the S-TREE algorithms is illustrated with data clustering and vector quantization examples, including a Gauss-Markov source benchmark and an image compression application. S-TREE performance on these tasks is compared with the standard tree-structured vector quantizer (TSVQ) and the generalized Lloyd algorithm (GLA). The image reconstruction quality with S-TREE2 approaches that of GLA while taking less than 10% of computer time. S-TREE1 and S-TREE2 also compare favorably with the standard TSVQ in both the time needed to create the codebook and the quality of image reconstruction.Office of Naval Research (N00014-95-10409, N00014-95-0G57

Stock Picking via Nonsymmetrically Pruned Binary Decision Trees

Stock picking is the field of financial analysis that is of particular interest for many professional investors and researchers. In this study stock picking is implemented via binary classification trees. Optimal tree size is believed to be the crucial factor in forecasting performance of the trees. While there exists a standard method of tree pruning, which is based on the cost-complexity tradeoff and used in the majority of studies employing binary decision trees, this paper introduces a novel methodology of nonsymmetric tree pruning called Best Node Strategy (BNS). An important property of BNS is proven that provides an easy way to implement the search of the optimal tree size in practice. BNS is compared with the traditional pruning approach by composing two recursive portfolios out of XETRA DAX stocks. Performance forecasts for each of the stocks are provided by constructed decision trees. It is shown that BNS clearly outperforms the traditional approach according to the backtesting results and the Diebold-Mariano test for statistical significance of the performance difference between two forecasting methods.decision tree, stock picking, pruning, earnings forecasting, data mining

Measurement of body temperature and heart rate for the development of healthcare system using IOT platform

Health can be define as a state of complete mental, physical and social well-being and not merely the absence of disease or infirmity according to the World Health Organization (WHO) [1]. Having a healthy body is the greatest blessing of life, hence healthcare is required to maintain or improve the health since the healthcare is the maintenance or improvement of health through the diagnosis, prevention, and treatment of injury, disease, illness, and other mental and physical impairments in human beings. The novel paradigm of Internet of Things (IoT) has the potential to transform modern healthcare and improve the well-being of entire society [2]. IoT is a concept aims to connec

An ergonomics analysis of manual versus chainsaw high ladder pruning of Pinus radiata in New Zealand : a thesis in partial fulfilment of the requirements for the degree of Master of Philosophy at Massey University

Two methods of ladder pruning Pinus radiata from 4.5 - 6.0 metres were compared using a cost-benefit approach within a framework provided by ergonomics. Chainsaw pruning is practiced in areas of New Zealand where large branches occur. The objectives of the research were to compare the costs and benefits of the two pruning techniques and provide recommendations as to whether or not the practice of chainsaw pruning should continue. These objectives were achieved by comparing the risk of injury, the physiological costs, the musculoskeletal costs, the productivity and the quality associated with the use of the two techniques. The general methods used to assess the relative costs and benefits of the two techniques were: l. Numeric descriptions of the 'risk' involved with each method of pruning 2. The use of a relative heart rate index to compare the physiological cost of the two techniques 3. Using questionnaires focusing on musculoskeletal pain and discomfort to assess any relative differences between the two techniques 4. Using continuous time study to quantify any difference in labour productivity between the two techniques 5. Sampling pruned trees to assess differences in the quality of work between manual and chainsaw pruning The research concludes that although both methods of pruning are hazardous, chainsaw pruning is more hazardous than manual pruning. Chainsaw and manual pruning were found to have the same physiological costs. Findings of the research indicate that manual pruning is not associated with a higher prevalence of musculoskeletal discomfort than chainsaw pruning on a yearly basis, although it is associated with a greater relative increase in BPD on a day to day basis and that this may lead to the development of musculoskeletal disease. Chainsaw pruning was found to be significantly more productive than manual pruning, although this was at the cost of quality. The research concludes by recommending that the use of chainsaw pruning should be limited to areas where the branches are demonstrably large. Further research is called for to compare the physiological and musculoskeletal costs of manual pruning in plantation areas of both large and small branch sizes. Further research is called for to compare the safety of two methods of chainsaw pruning with the use of the technique of wrapping one leg around the tree as opposed to not wrapping the leg around the tree. Research to investigate new ladder designs which are safer to use in the New Zealand forest environment is also called for

ENERGY EFFICIENCY AND LIFE CYCLE ANALYSIS OF ORGANIC AND CONVENTIONAL OLIVE GROVES IN THE MESSARA VALLEY, CRETE, GREECE.

Environmental Impacts of agricultural activities have to be assessed in order to address cultural practices and the type of farming that are best suited to avoid the trade-off between the Ecology and the Economy. Furthermore, this study, comparing the environmental impacts with the Life Cycle Analysis (LCA) of Organic and Conventional olive oil production, is proposing to consider the relationship between the Energy Efficiency and the environmental impacts, notably the Climate Change (Global Warming contribution through Greenhouse Gas emission). The LCA is used to take into account the impacts of the production system from the Cradle (input production) to the Farm gate (final farm product) and considers 7 environmental impacts potential categories: Global Warming, Acidification, Eutrophication, Biodiversity, Erosion, Resource depletion, Ground water depletion. The study also assesses the Energy efficiency of both systems. The results show a clear difference between organic and conventional production, namely a two-fold improvement of the energy efficiency in the organic production. Even if the differences are reduced when the results are calculated on the yield rather than the area, the organic methods have a far smaller contribution to Global warming, Eutrophication, Biodiversity loss, Soil loss, Groundwater depletion and Energy use whereas, the Acidification potential is comparable in both cases. The study recommends encouraging some of the cultural practices that are used in the organic farming methods in order to reduce the burden of agriculture on the local and global ecology as well as the natural resources