Experiment Based Teaching of Solar Cell Operation and Characterization Using the SolarLab Platform

Experiment based teaching methods are a great way to get students involved and interested in almost any topic. This paper presents such a hands-on approach for teaching solar cell operation principles along with characterization and modelling methods. This is achieved with the SolarLab platform which is a laboratory teaching tool developed at Transylvania University of Brasov. Using this platform, solar cells can be characterized under various illumination, temperature and angle of light incidence. Additionally, the SolarLab platform includes guided exercises and intuitive graphical user interfaces for exploring different solar cell principles and topics. The exercises presented in the current paper have been adapted from the original exercises developed for the SolarLab platform and are currently included in the Photovoltaic Power Systems courses (MSc and PhD level) taught at the Department of Energy Technology, Aalborg University.<br/

Machine learning in prediction of intrinsic aqueous solubility of drug‐like compounds: Generalization, complexity, or predictive ability?

We present a collection of publicly available intrinsic aqueous solubility data of 829 drug‐like compounds. Four different machine learning algorithms (random forests [RF], LightGBM, partial least squares, and least absolute shrinkage and selection operator [LASSO]) coupled with multistage permutation importance for feature selection and Bayesian hyperparameter optimization were used for the prediction of solubility based on chemical structural information. Our results show that LASSO yielded the best predictive ability on an external test set with a root mean square error (RMSE) (test) of 0.70 log points, an R2(test) of 0.80, and 105 features. Taking into account the number of descriptors as well, an RF model achieves the best balance between complexity and predictive ability with an RMSE(test) of 0.72 log points, an R2(test) of 0.78, and with only 17 features. On a more aggressive test set (principal component analysis [PCA]‐based split), better generalization was observed for the RF model. We propose a ranking score for choosing the best model, as test set performance is only one of the factors in creating an applicable model. The ranking score is a weighted combination of generalization, number of features, and test performance. Out of the two best learners, a consensus model was built exhibiting the best predictive ability and generalization with RMSE(test) of 0.67 log points and a R2(test) of 0.81

Outdoor Electroluminescence Acquisition Using a Movable Testbed

The experimentation with a movable outdoor electroluminescence (EL) testbed is performed in this work. For EL inspections of PV power plants, the fastest scenario will include the use of unmanned aerial vehicle (UAV) performing image acquisition in continuous motion. With this motivation, we investigate the EL image quality of an acquisition in motion and the extent of image processing required to correct scene displacement. The results show processed EL images with a high level of information even when acquired at 1 m/s camera speed and at frame rate of 120 fps.</p

Heterogeneity, high performance computing, self-organization and the Cloud

This open access book addresses the most recent developments in cloud computing such as HPC in the Cloud, heterogeneous cloud, self-organising and self-management, and discusses the business implications of cloud computing adoption. Establishing the need for a new architecture for cloud computing, it discusses a novel cloud management and delivery architecture based on the principles of self-organisation and self-management. This focus shifts the deployment and optimisation effort from the consumer to the software stack running on the cloud infrastructure. It also outlines validation challenges and introduces a novel generalised extensible simulation framework to illustrate the effectiveness, performance and scalability of self-organising and self-managing delivery models on hyperscale cloud infrastructures. It concludes with a number of potential use cases for self-organising, self-managing clouds and the impact on those businesses

Evaluation methods for comparing energy savings due to variable speed pumping in wastewater applications

The Master Thesis work has been carried out at Xylem HQ, Sundbyberg, Sweden in collaboration with Linköping University, Department of Management and Engineering. The work was to evaluate in different ways energy savings in wastewater pumping stations and conclude what is the discrepancy between them, emphasizing on the theoretical model and measured data. Two pump stations were chosen to be modeled by mathematical calculations based on theoretical pump and system curves. Based on the same inputs, a commercial tool was used to calculate energy savings. Moreover, theoretical curves and variable speed drives were combined into an own developed testing platform in LabView, as an alternative evaluation solution. Finally, measured data was collected and used in a specific energy algorithm, designed to have as inputs water level and energy. In term of method accuracy, initial assumptions are wrong. For a given frequency, the results show similar values for all four evaluation methods. Also, variable speed is confirmed as a good control philosophy for less energy use than direct online

Decentralized Cloud Computing

This thesis proposes a decentralized Cloud platform, where Services can use infrastructure and hardware accelerators from a network of private compute resources managed through Smart Contracts deployed on a public Blockchain. This is achieved by leveraging and extending existing open-source technologies for Cloud Orchestration and designing a decentralized resource selection mechanism and scheduling protocols for resource allocation. The decentralized resource selection mechanism is facilitated by a Smart Contract on a public Blockchain and a Self-Organizing Self-Managing Resource Management System. The main contribution of this part is the investigation of the operational constraints and costs associated with outsourcing the selection logic to the Smart Contract. Moreover, the selection mechanism is optimized to increase the throughput of scheduling decisions. The platform allows for the definition, composition, optimization, and deployment of Cloud Services. It improves the state of the art by allowing a user to design applications composed of abstract services, for which explicit implementations and resources are selected by our platform, depending on the user constraints and resource availability. This contribution improves the flexibility of both the Cloud user and the Cloud provider, allowing for a more efficient Cloud. The novel resource management framework allows for the self-organization of collaborative resource management components. Each component makes use of a Suitability Index in order to take self-organization decisions and guide resource requests to the most suitable resource given the system state. Experimental evaluation shows that a small provisioning delay is incurred by our System compared to traditional on-premises deployment, yet no significant degradation can be observed in relation to the performance of the Services under investigation. Another important contribution is the concept of Component Administration Networks, designed to monitor and enforce a set of replicas that deal with storing system state data that can be used to recover in case of component or service failure. The integrated results of the thesis allow for the creation a free, decentralized, market for Cloud Applications to be deployed on different types of infrastructure. The resources are managed efficiently, according to high level business metrics, and the fault tolerance of Applications is enforced

Experiment based teaching of solar cell operation and characterization using the SolarLab platform

Experiment based teaching methods are a great way to get students involved and interested in almost any topic. This paper presents such a hands-on approach for teaching solar cell operation principles along with characterization and modelling methods. This is achieved with the SolarLab platform which is a laboratory teaching tool developed at Transylvania University of Brasov. Using this platform, solar cells can be characterized under various illumination, temperature and angle of light incidence. Additionally, the SolarLab platform includes guided exercises and intuitive graphical user interfaces for exploring different solar cell principles and topics. The exercises presented in the current paper have been adapted from the original exercises developed for the SolarLab platform and are currently included in the Photovoltaic Power Systems courses (MSc and PhD level) taught at the Department of Energy Technology, Aalborg University.<br/

Machine Learning in Prediction of Intrinsic Aqueous Solubility of Drug-like Compounds: Generalization, Complexity or Predictive Ability?

Here, we present a collection of publicly availableintrinsic aqueous solubility data of 829 drug-likecompounds. Four different machine learning algorithms(random forest, light GBM, partial least squares andLASSO) coupled with multi-stage permutationimportance for feature selection and Bayesian hyperparameter optimization were employed for theprediction of solubility based on chemical structuralinformation. Our results have shown that LASSOyielded the best predictive ability on an external test setwith and RMSE(test) of 0.70 log points and 105 featuresin the model. Taking into account the number ofdescriptors as well, an RF model achieved the bestbalance between complexity and predictive ability withan RMSE(test) of 0.72 with only 17 features. Wepropose a ranking score for choosing the best model, astest set performance is only one of the factors in creatingan applicable model. The ranking score is a weightedcombination of generalization, number of featuresinvolved and test set performance The data related to this paper can be downloaded from 10.5281/zenodo.3968754</div