An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Modelling of a post-combustion CO₂ capture process using neural networks

This paper presents a study of modelling post-combustion CO₂ capture process using bootstrap aggregated neural networks. The neural network models predict CO₂ capture rate and CO₂ capture level using the following variables as model inputs: inlet flue gas flow rate, CO₂ concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. In order to enhance model accuracy and reliability, multiple feedforward neural network models are developed from bootstrap re-sampling replications of the original training data and are combined. Bootstrap aggregated model can offer more accurate predictions than a single neural network, as well as provide model prediction confidence bounds. Simulated CO₂ capture process operation data from gPROMS simulation are used to build and verify neural network models. Both neural network static and dynamic models are developed and they offer accurate predictions on unseen validation data. The developed neural network models can then be used in the optimisation of the CO₂ capture process

Potential of X-ray computed tomography for 3D anatomical analysis and microdensitometrical assessment in wood research with focus on wood modification

Studying structure and chemistry of wood and wood-based materials is the backbone of all wood research and many techniques are at hand to do so. A very valuable modality is X-ray computed tomography (CT), able to non-destructively probe the three-dimensional (3D) structure and composition. In this paper, we elaborate on the use of Nanowood, a flexible multi-resolution X-ray CT set-up developed at UGCT, the Ghent University Centre for X-ray Tomography. The technique has been used successfully in many different fields of wood science. It is illustrated how 3D structural and microdensitometrical data can be obtained using different scan set-ups and protocols. Its potential for the analysis of modified wood is exemplified, e.g. for the assessment of wood treated with hydrophobing agents, localisation of modification agents, pathway analysis related to functional tissues, dimensional changes due to thermal treatment, etc. Furthermore, monitoring of transient processes is a promising field of activity too

Seismic Response of a Platform-Frame System with Steel Columns

Timber platform-frame shear walls are characterized by high ductility and diffuse energy dissipation but limited in-plane shear resistance. A novel lightweight constructive system composed of steel columns braced with oriented strand board (OSB) panels was conceived and tested. Preliminary laboratory tests were performed to study the OSB-to-column connections with self-drilling screws. Then, the seismic response of a shear wall was determined performing a quasi-static cyclic-loading test of a full-scale specimen. Results presented in this work in terms of force-displacement capacity show that this system confers to shear walls high in-plane strength and stiffness with good ductility and dissipative capacity. Therefore, the incorporation of steel columns within OSB bracing panels results in a strong and stiff platform-frame system with high potential for low- and medium-rise buildings in seismic-prone areas

Reinforcement learning for efficient network penetration testing

Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The proposed system is named Intelligent Automated Penetration Testing System (IAPTS) consisting of a module that integrates with industrial PT frameworks to enable them to capture information, learn from experience, and reproduce tests in future similar testing cases. IAPTS aims to save human resources while producing much-enhanced results in terms of time consumption, reliability and frequency of testing. IAPTS takes the approach of modeling PT environments and tasks as a partially observed Markov decision process (POMDP) problem which is solved by POMDP-solver. Although the scope of this paper is limited to network infrastructures PT planning and not the entire practice, the obtained results support the hypothesis that RL can enhance PT beyond the capabilities of any human PT expert in terms of time consumed, covered attacking vectors, accuracy and reliability of the outputs. In addition, this work tackles the complex problem of expertise capturing and re-use by allowing the IAPTS learning module to store and re-use PT policies in the same way that a human PT expert would learn but in a more efficient way

Learning-based Analysis on the Exploitability of Security Vulnerabilities

The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing the accuracy of each model, dynamically training the models on a rolling window of training data, and filtering the training data by various features. Of the chosen models, the deep neural network and the support vector machine show the highest accuracy (approximately 94% and 93%, respectively), and could be developed by future researchers into an effective tool for vulnerability analysis

The potential of X-ray tomography for research on modified wood

This paper exemplifies the use of X-ray tomography hardware and accompanying software for research on modified wood. Structural details on different spatial scales as well as time-dependent phenomena can be visualized, also including quantification of these structural details and probable changes due to treatment. As such, the impact of hydrophobing agents can be studied both structurally as well as regarding the efficacy of their water repellence effect, the anatomical changes of heat treated wood can be visualised and quantified at cell wall level as well as the density changes caused by these treatments, etc. X-ray tomography as such is the pre-eminent tool of the future for wood scientists in general and wood modification researchers more specifically