Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Improving Electricity Distribution System State Estimation with AMR-Based Load Profiles

The ongoing battle against global warming is rapidly increasing the amount of renewable power generation, and smart solutions are needed to integrate these new generation units into the existing distribution systems. Smart grids answer this call by introducing intelligent ways of controlling the network and active resources connected to it. However, before the network can be controlled, the automation system must know what the node voltages and line currents defining the network state are.Distribution system state estimation (DSSE) is needed to find the most likely state of the network when the number and accuracy of measurements are limited. Typically, two types of measurements are used in DSSE: real-time measurements and pseudomeasurements. In recent years, finding cost-efficient ways to improve the DSSE accuracy has been a popular subject in the literature. While others have focused on optimizing the type, amount and location of real-time measurements, the main hypothesis of this thesis is that it is possible to enhance the DSSE accuracy by using interval measurements collected with automatic meter reading (AMR) to improve the load profiles used as pseudo-measurements.The work done in this thesis can be divided into three stages. In the first stage, methods for creating new AMR-based load profiles are studied. AMR measurements from thousands of customers are used to test and compare the different options for improving the load profiling accuracy. Different clustering algorithms are tested and a novel twostage clustering method for load profiling is developed. In the second stage, a DSSE algorithm suited for smart grid environment is developed. Simulations and real-life demonstrations are conducted to verify the accuracy and applicability of the developed state estimator. In the third and final stage, the AMR-based load profiling and DSSE are combined. Matlab simulations with real AMR data and a real distribution network model are made and the developed load profiles are compared with other commonly used pseudo-measurements.The results indicate that clustering is an efficient way to improve the load profiling accuracy. With the help of clustering, both the customer classification and customer class load profiles can be updated simultaneously. Several of the tested clustering algorithms were suited for clustering electricity customers, but the best results were achieved with a modified k-means algorithm. Results from the third stage simulations supported the main hypothesis that the new AMR-based load profiles improve the DSSE accuracy.The results presented in this thesis should motivate distribution system operators and other actors in the field of electricity distribution to utilize AMR data and clustering algorithms in load profiling. It improves not only the DSSE accuracy but also many other functions that rely on load flow calculation and need accurate load estimates or forecasts

Challenges and Open Questions of Machine Learning in Computer Security

This habilitation thesis presents advancements in machine learning for computer security, arising from problems in network intrusion detection and steganography. The thesis put an emphasis on explanation of traits shared by steganalysis, network intrusion detection, and other security domains, which makes these domains different from computer vision, speech recognition, and other fields where machine learning is typically studied. Then, the thesis presents methods developed to at least partially solve the identified problems with an overall goal to make machine learning based intrusion detection system viable. Most of them are general in the sense that they can be used outside intrusion detection and steganalysis on problems with similar constraints. A common feature of all methods is that they are generally simple, yet surprisingly effective. According to large-scale experiments they almost always improve the prior art, which is likely caused by being tailored to security problems and designed for large volumes of data. Specifically, the thesis addresses following problems: anomaly detection with low computational and memory complexity such that efficient processing of large data is possible; multiple-instance anomaly detection improving signal-to-noise ration by classifying larger group of samples; supervised classification of tree-structured data simplifying their encoding in neural networks; clustering of structured data; supervised training with the emphasis on the precision in top p% of returned data; and finally explanation of anomalies to help humans understand the nature of anomaly and speed-up their decision. Many algorithms and method presented in this thesis are deployed in the real intrusion detection system protecting millions of computers around the globe

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words