Linköping Electronic Conference Proceedings
Not a member yet
1113 research outputs found
Sort by
The Enigma of Franceschi’s Falso Scontro
Get PDFFranceschi’s recently discovered folder of writings sheds new light on his Cifra delle Caselle, and Falso Scontro ciphers; in fact he had in mind a uera ziffra (true cipher) a concept that, in a suggestive parallel, resembles the perfect Shannon cryptosystem, while the Falso Scontro (fake key) cipher closely resembles Vernam cipher
Language learning analytics : designing and testing new functional complexity measures in L2 writings
Get PDFThis paper presents the initial stage in the design of an ICALL system. The objective is to develop a system that automatically generates linguistic analytics of L2 learner writings. Student texts will be processed with NLP tools producing different types of textual measures. We present the design of a new functional complexity metric aiming to capture the paradigmatic competition between forms mapped to the same communicative function, i.e. microsystems. More precisely, we analyze the variations of the FOR and TO prepositions in terms of frequency and probability of occurrence. Relative frequency shows significant correlations with CEFR levels suggesting its possible use in an analytics report system. More work is required to extend the approach to other microsystems
Application of Multivariate Data Analysis of Raman Spectroscopy Spectra of 2-oxazolidinone
Get PDFChemical absorption of carbon dioxide (CO2) using amine solution is considered as the readiest technology available for capturing CO2 gas from industrial processes. The well-known amine for this process is 2-aminoethanol (MEA) which is normally mixed with water to a typical concentration of 30 wt%. MEA degrades over time producing non-reactive chemicals such as 2-oxazolidinone (OZD) due to exposure to impurities and high process temperature. It is thus important to find a suitable method for OZD qualification and quantification. In this work, we approach this challenge by means of Raman spectroscopy and multivariate data analysis. We started by collecting Raman spectra of 40 OZD samples and applying Principal omponent Analysis to study these samples
Intelligent Micro Grid Controller Development for Hardware-in-the-loop Micro Grid Simulation Subject to Cyber-Attacks
Get PDFThis paper develops Hardware-in-the-loop (HIL) simulation against cyber attacks. We design a light-weight intelligent electronic device (IED) that performs Micro Grid Controller (MGC), interfaces are developed based on International Electrotechnical Commission (IEC) 61850 GOOSE protocol from/to the real-time simulation and the MGC. They are executed on two equipment stages, Field Programmable Gate Array (FPGA) and BeagleBoneBlack. CSIL versus CHIL tests are used to evaluate the Micro Grid (MG) behavior against different cyber attacks. We also evaluate the MGC designed control function in accordance with IEC 61850 GOOSE protocol. The results show that the light-weight MGC approach and data modeling of various IEC 61850 predefined data objects, data attributes and logical nodes (LNs) are correct for the design of the power balance control/protection function against cyber attacks in various cyber-attack case studies
Extended ATM for Seamless Travel (X-TEAM D2D)
Get PDFX-TEAM D2D project is focused on integrating Air Traffic Management and Urban Air Mobility into an overall multimodal transport network to address the potential increase in efficiency of the overall transportation system in the future, considering the operational domain of the urban and extended urban environment up to a regional extent and passenger-centric perspective. This paper presents the analysis of the Door to Airport trajectory of business passengers until 2035. The results indicate the system's expected performance in 2035 under normal and disrupted scenarios providing insight on the expected impact of future technologies
Feasibility Study on the Use of Electrolyzers for Short term Energy Storage
Get PDFElectricity grid flexibility is vital for renewable energy to be used effectively. Power-to-gas technologies are investigated to connect electricity grid to gas grid and to tackle capacity challenges. Grid management expenses consist of redispatch and feed-in management. These management procedures, next to being costly, cause a significant energy loss. Proton-exchange membrane electrolyzer installations were studied to reduce these expenses and recover energy. The change in the levelized cost of hydrogen production with varying electrolyzer capacities was presented. The sensitivity of the levelized cost and net present value with respect to installation costs, maintenance costs, and electricity prices were investigated. While the electricity prices have the most significant effect on the levelized cost of hydrogen production, the net present value was affected considerably by the hydrogen selling price. Possible energy savings were calculated between 2 – 23 GWh for 2, 5, 10, 20 MW installations. The annual grid management expense savings were in the range of 0.2 – 2.3 million Euros, increasing with the increasing electrolyzer capacity
Sensitivity and Uncertainty Analysis in a Circulating Fluidized Bed Reactor Modeling
Get PDFAs in many real applications, in the world of fine powders and small particles, there are uncertainties and vagueness in the parameters such as particle size, sphericity, initial solid void fraction, envelope density, etc. In some cases, there are different methods to measure a parameter, such as a particle size that depends on the method (based on length, weight, and volume); the measured values may be significantly different from each other. Therefore, there is no crisp or exactly known parameter in many cases because of the fine powders' inherent uncertain nature. On the other hand, being characteristic of the dynamic systems, physical parameters such as temperature and pressure fluctuate but can be kept in an acceptable range, affecting the main design parameters such as fluid density and dynamic viscosity. The most traditional tools and methods for simulating, modeling, and reasoning are crisp, deterministic, and precise, but these values are estimated or changing (randomly or stochastically). Several approaches can describe this phenomenon. Moreover, when it comes to uncertainties, mathematical tools are probably the best solutions. With the fuzzy set theory method, linguistic variables or ranges can be converted to mathematical expressions, and consequently, instead of crisp values, these can be applied to the equations. The uncertainty analysis can be more important when the model is susceptible to one parameter. A preliminary sensitivity analysis on a fluidized bed application has shown that the solid void fraction has the highest, and the fluid density has the lowest sensitivity to its operation. The theoretical approach has been validated by CPFD simulation using Barracuda v20.1.0
Intelligent Epidemiological Models for COVID-19
Get PDFThe coronavirus COVID-19 is affecting around the world with strong differences between countries and regions. Extensive datasets are available for visual inspection and downloading. The material has limitations for phenomenological modelling but data-based methodologies can be used. This research focuses on intelligent modelling on the basis of these datasets. The methodology has been tested in the analysis of daily new confirmed COVID-19 cases and deaths in six countries. The datasets are studied per million people to get comparable indicators. Nonlinear scaling brings the data of different countries to the same scale and linear interactions represent the varying operating conditions well. The same approach operates for both the confirmed cases and deaths and can be used for any country or group of people. The effects of the vaccinations were clearly shown at the end of the analyzed period. During the pandemic, the scaling functions expanded for the confirmed cases but remained practically unchanged for the confirmed deaths which is consistent with increasing testing. Limitations are seen if there are too many interacting things, e.g. several infection transmission chains which are in different stages. The feasibility analysis needs to be extended to the modelling with inputs. The presented approach is promising for this wider analysis
Checking data informativity as the first step in data-driven modeling – case study
Get PDFThis paper reviews and introduces the strategies for testing a given dataset sampled from an unknown dynamic process to determine if it is sufficiently informative to model the system’s behavior. The presented tests should be done as the first step in data-driven modeling to avoid an endless search for a proper model which may not exist based on the available data. It is unrealistic that available data holds complete information about the system at hand. The tests also allow us to estimate how good the established model can be. Finally, the presented methodologies are applied to an actual process as the case study: modeling the decarbonization section in an ammonia plant
Predictive Maintenance of Pumps at ‘Den Magiske Fabrikken’, Using Machine Learning Techniques
Get PDFIn this work, we investigate machine learning methods to predict the failures of progressive cavity pumps (PCP). The PCPs are located in a biogas plant, Den Magiske Fabrikken, in Norway, which is transforming food waste and animal manure to biogas and biofertilizer. Available measurements were pump on-signal, speed, current, torque and control signal, inlet flow, inlet pressure and outlet pressure, and several vibrations derived signals.
Five categories were defined to categorize the operation of the pumps as: “stopped”, “normal running”, “7 days from failure”, “1 day from failure” and “1 hour from failure”. The objective was to train a Machine Learning model to predict these categories. The data was pre-processed to clean gross outliers and scale the signals using different techniques.
This paper presents results from the same Long Short-Term Memory (LSTM) model using two different approaches for scaling the data. The results are evaluated using confusion matrices where one scaling method clearly improves the results when testing on new data points. Further work is presently being carried out to implement the selected methods in real-time and to generalize the model (Holm, 2022)