Peak Performance – Remote Memory Revisited

Many database systems share a need for large amounts of fast storage. However, economies of scale limit the utility of extending a single machine with an arbitrary amount of memory. The recent broad availability of the zero-copy data transfer protocol RDMA over low-latency and high throughput network connections such as InfiniBand prompts us to revisit the long-proposed usage of memory provided by remote machines. In this paper, we present a solution to make use of remote memory without manipulation of the operating system, and investigate the impact on database performance

Data generation and model usage for machine learning-based dynamic security assessment and control

The global effort to decarbonise, decentralise and digitise electricity grids in response to climate change and evolving electricity markets with active consumers (prosumers) is gaining traction in countries around the world. This effort introduces new challenges to electricity grid operation. For instance, the introduction of variable renewable energy generation like wind and solar energy to replace conventional power generation like oil, gas, and coal increases the uncertainty in power systems operation. Additionally, the dynamics introduced by these renewable energy sources that are interfaced through converters are much faster than those in conventional system with thermal power plants. This thesis investigates new operating tools for the system operator that are data-driven to help manage the increased operational uncertainty in this transition. The presented work aims to an- swer some open questions regarding the implementation of these machine learning approaches in real-time operation, primarily related to the quality of training data to train accurate machine- learned models for predicting dynamic behaviour, and the use of these machine-learned models in the control room for real-time operation. To answer the first question, this thesis presents a novel sampling approach for generating ’rare’ operating conditions that are physically feasible but have not been experienced by power systems before. In so doing, the aim is to move away from historical observations that are often limited in describing the full range of operating conditions. Then, the thesis presents a novel approach based on Wasserstein distance and entropy to efficiently combine both historical and ’rare’ operating conditions to create an enriched database capable of training a high- performance classifier. To answer the second question, this thesis presents a scalable and rigorous workflow to trade-off multiple objective criteria when choosing decision tree models for real-time operation by system operators. Then, showcases a practical implementation for using a machine-learned model to optimise power system operation cost using topological control actions. Future research directions are underscored by the crucial role of machine learning in securing low inertia systems, and this thesis identifies research gaps covering physics-informed learning, machine learning-based network planning for secure operation, and robust training datasets are outlined.Open Acces

Applying Artificial Intelligence for Operating System Fingerprinting

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] In the field of computer security, the possibility of knowing which specific version of an operating system is running behind a machine can be useful, to assist in a penetration test or monitor the devices connected to a specific network. One of the most widespread tools that better provides this functionality is Nmap, which follows a rule-based approach for this process. In this context, applying machine learning techniques seems to be a good option for addressing this task. The present work explores the strengths of different machine learning algorithms to perform operating system fingerprinting, using for that, the Nmap reference database. Moreover, some optimizations were applied to the method which brought the best results, random forest, obtaining an accuracy higher than 96%.CITIC, as a research center accredited by the Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported—80% through ERDF, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades (Grant ED431G 2019/01). This project was also supported by the “Consellería de Cultura, Educación e Ordenación Universitaria” via the Consolidation and Structuring of Competitive Research Units–Competitive Reference Groups (ED431C 2018/49) and the COST Action 17124 DigForAsp, supported by COST (European Cooperation in Science and Technology, www.cost.eu, (accessed on 25 October 2021)).Xunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2018/4

Improving the Knowledge of Anesthesia Providers on Preventing and Managing Intraoperative Anesthesia Machine Failure

ABSTRACT Background: The anesthesia machine is important medical equipment in the operating room that provides safe anesthesia delivery to patients. Despite improvement from a mechanical to a computerized electronic device, on rare occasions, the anesthesia equipment system can fail. The anesthesia machine delivers oxygen and anesthetic gas to patients during surgical procedures. Breathing circuit leaks or failure during surgical cases have been reported to the Food and Drug Administration (FDA). Injury resulted from anesthesia equipment malfunction can be detrimental to patient outcome. The objective of this quality improvement project is to improve the knowledge of anesthesia providers on preventing and managing intraoperative anesthesia machine failure. Methods: An extensive database search that included Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, Medline, the National Center for Biotechnology Information (NCBI), and Google Scholar was used to searching for articles relevant to preoperative anesthesia machine failure. Results: The 6 articles selected for this literature review focused on analyzing anesthesia machine faults, identifying errors and equipment failure, and educating or training providers on addressing anesthesia machine failure

APLIKASI PENCATATAN KERUSAKAN DAN PERBAIKAN MESIN PENDINGIN BERBASIS ANDROID

Showcase Cooler is a cooling machine that is used to preserve drinks and food so that they can be stored longer. In making repairs to the cooling machine currently still using paper which results in errors in recording data, wasteful use of paper because they have to recap data to be distributed to other parts. Android is a complex technology because it uses open source as the basis for the project that makes the smartphone operating system widely used today. The support for the software used in developing this application, namely Andoid Studio is a development software for developing android applications, Java functions as a programming language used to create software, and SQLite is a database that uses the user's internal storage device. This study uses the Black Box Testing method or functional testing because testing the software used without having to know the internal structure of the code or program. The resulting application is an android-based damage and repair recording system that is used for maintenance. Utilization of this technology so that maintenance can record repair damage to the machine using an Android-based smartphone in order to save on textbooks or paper media

Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data

Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data

A Curriculum Development Project for IBM Linux in Academia Program

This paper introduces the IBM Linux in Academia program and a curriculum development project initiated by the authors for the program. The service of IBM Linux in Academia program is based on the Linux virtual service concept in which a mainframe computer is partitioned into many Linux images supported by IBM’s Virtual Machine Operating System. On the IBM S/390 system, each image acts as an independent Linux server. This free service saves the acquisition and management cost of running multiple physically separated servers for participating universities. The curriculum development project intends to create and share curriculum materials for e-Business related courses among participants. The main IBM software used in this project includes DB2 Universal Database and WebSphere. The main objective in the first stage of this project is to develop a data warehouse generator to manipulate a large read-only database obtained from a real world health care application supplied by IBM. Through a web based user interface, an instructor could flexibly create a data warehouse using the Account Data Model developed by some of the authors from the read-only database with the desirable size and attributes to support pedagogical needs. Other aspects of the project are also addressed

A Machine-learning based Probabilistic Perspective on Dynamic Security Assessment

Probabilistic security assessment and real-time dynamic security assessments (DSA) are promising to better handle the risks of system operations. The current methodologies of security assessments may require many time-domain simulations for some stability phenomena that are unpractical in real-time. Supervised machine learning is promising to predict DSA as their predictions are immediately available. Classifiers are offline trained on operating conditions and then used in real-time to identify operating conditions that are insecure. However, the predictions of classifiers can be sometimes wrong and hazardous if an alarm is missed for instance. A probabilistic output of the classifier is explored in more detail and proposed for probabilistic security assessment. An ensemble classifier is trained and calibrated offline by using Platt scaling to provide accurate probability estimates of the output. Imbalances in the training database and a cost-skewness addressing strategy are proposed for considering that missed alarms are significantly worse than false alarms. Subsequently, risk-minimised predictions can be made in real-time operation by applying cost-sensitive learning. Through case studies on a real data-set of the French transmission grid and on the IEEE 6 bus system using static security metrics, it is showcased how the proposed approach reduces inaccurate predictions and risks. The sensitivity on the likelihood of contingency is studied as well as on expected outage costs. Finally, the scalability to several contingencies and operating conditions are showcased.Comment: 42 page