Graph convolution reinforcement learning for active wake control in windfarms: Application of a multi-agent reinforcement learning algorithm

Wind energy, generated by windfarms, is playing an increasingly critical role in meeting current and future energy demands. windfarms, however, face a challenge due to the inherent flaw of wake-induced power losses when turbines are located in close proximity. Wakes, characterized by regions of turbulence and lower wind speed, are created as air...

Applying QMIX to Active Wake Control: Multi-Agent Reinforcement Learning

When multiple wind turbines are positioned close to one another, such as in a wind farm, wind turbines located downwind of other turbines are not 100% efficient due to wakes, negatively affecting the total power output of the wind farm. A way to mitigate the loss of power is to steer the wake away from the next turbine, which lowers the current...

Sailing the Wind: Evaluating the Impact of COMA on Multi-Agent Active Wake Control in Wind Farms: What is the effect of COMA on the problem of AWC compared to single-agent RL algorithms?

The close proximity of wind turbines to one another in a wind farm can lead to inefficiency in terms of power production due to wake effects. One technique to mitigate the losses is to veer from their individual optimal direction. As such, the wakes can be steered away from downstream turbines in order to increase the overall power output. Multi...

Mean Field Multi Agent Reinforcement Learning for Active Wake Control

The wake effect which is turbulence behind a wind turbine created when it extracts energy negatively impacts the power output of the downstream turbines. Active Wake Control can mitigate this effect, by rotating some turbines away from the wind. Previous research applied single agent reinforcement learning to apply Active Wake Control, show- ing...

Multi Agent Deep Deterministic Policy Gradient for Active Wake Control

In wind farms wind turbines are often placed close to each other. Each turbine generates a turbulent wake field, this field negatively affects subsequent turbines. This can cost more than 12% efficiency. To decrease this loss we can steer the turbines away from the wind direction, this will decrease the individual turbine power output, but can...

To identify a correlation between IMU and microphone data in earable computing with regards to chewing

This research explores the correlation between chewing activities and non-chewing activities using an Arduino microcontroller. Chewing samples are recorded by attaching the microcontroller to the back of the jaw, underneath the ear. The microcontroller collects data from a microphone capturing audio data of chewing sounds, as well as an Inertial...

Log-Based Behavioral System Model Inference Using Reinforcement Learning

System behavior models are highly useful for the developers of the system as they aid in system comprehension, documentation, and testing. Even though methods to obtain such models exist, e.g. profiling, tracing, source code inference and existing log-based inference methods, they can not successfully be applied to the case of large, real-time...

Inferring DFAs from Log Traces Using Community Detection

Large software systems today require increasingly complex models of their execution to aid the analysis of their behavior. Such execution models are impractical to compile by hand, and current approaches to their automated generation are either not generalizable or not scalable enough. This paper addresses this problem with a new approach based...

Modeling System Behaviour from Log Analysis Using Meta-Heuristic Search

Understanding the behaviour of a software system plays a key role in its development and maintenance processes. Unfortunately, accurate and concise models are not always available during development, due to the rapid changes in the structure and scale systems may undergo during this phase. Finite State Machines (FSM) are a natural and prevalent...

Inferring Log-Based Behavioural System Models using Markov Chains

An essential step of software development is obtaining an understanding of the behaviour of a system. Accurate state models of system behaviour might help software developers build such an understanding. There exist several techniques for automatically inferring models on system behaviour using log analysis, but these do not scale well for...

Removing redundant statements in amplified test cases

Amplified test cases created by DSpot and TestCube often contain unnecessary statements that impact the readability of the tests in question. As a part of the effort to make these amplified test cases more developer-friendly, we investigate (dynamic) slicing, taint analysis and static analysis as approaches to remove redundant statements. In...

Developer-Friendly Test Cases: Detection and Removalof Unnecessary Casts

Test-cube is a tool that focuses on developer-friendly test amplification. Test amplification is a technique to improve a test suite by generating new tests based on manually written ones. Currently, these generated tests contain much redundant casting. Our study aimed to improve the readability of these generated test cases by reducing...

Leveraging the Wisdom of the Crowds

Wisdom of the crowds is the idea that groups of people can collectively make wise decisions. Research suggests that these crowds can even outsmart experts. To gather the wisdom of the crowds, this project utilizes a prediction market. To successfully gather the wisdom of the crowds, a predictionmarket has to overcome serious challenges, such as...

Improving Test Case Generation for RESTful APIs through Seeded Sampling

To validate the quality of software, test cases are used. These test cases are often manually-written, which is labor-intensive. To avoid this problem, automated software testing was invented. Search-based software testing is a useful tool for developers to automatically generate test cases. However, improvements are still needed to create test...

Multi-Objective Hill Climbing for Automated RESTful API Test Case Generation

RESTful APIs tend to be difficult to manually write tests for. To help developers with this tedious task, a tool called EvoMaster has already been developed, to aim to automate the generation of test cases for RESTful APIs. The automation of test cases can be modeled as a multi-objective optimization problem. The existing tool EvoMaster has...

Guiding automated system test generation for RESTful APIs using log statements

Automated generation of system tests for RESTful APIs has been extensively investigated. Previous investigations use either a white box or a blackbox approach, wherein the quality of the test cases can be assessed on the HTTP response in the prior and also on the results of byte-code analysis in the latter. Both approaches are limited however,...

Preserving Inter-gene Relations During Test Case Generation using Intelligent Evolutionary Operators

Randomized variational operators can be very disruptive to the search process, especially when there exist dependencies between the variables under search. Within test-cases, these dependencies exist as well. This makes it interesting to evaluate the benefits of preserving these dependencies during test-case generation.<br/><br/>In this paper,...