We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-of-the-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study.

The purpose of this thesis was to study the properties of electronic current and voltage transformers, choose the method for temperature cycle accuracy test and the development of the measuring software. Measuring software was realized in LabVIEW version 8.2. The first part describe the topic of this thesis. The second part describes various kinds of sensors and describtion of functions and specifications. The third part describe programming system LabVIEW. The fourth describe design of connection supply and measurement part of circuit. The fifth part describes used instruments. The sixth part is about used methods measuring. In the seven part is the description of the functions of this program and its control. In eight part is evaluated measurement uncertainty. In the last part are shown examples of the application program.

The diploma thesis is dedicated to modern kiosk-type transformer substations PET, that are used when modernizing distribution network. Since such substations are placed on publicly accessible places it is really important that they are safe for service staff as well as for the public. Apart from the highest characteristic values stress is put upon the safety. Right construction, functionality and safety is verified using the type-tests as stated in ČSN EN 62271-202. In the last part of the thesis there are the methods and procedures of type-testing described.

This thesis presents and describes new global trends among build and operation of datacenters. Further it contains practical application of particular examples, and the theory is supplemented by new findings from given field.

This thesis is devoted to analysis of the diagnostic methods used in practice. It is divided into two parts: theoretical and practical. The theoretical part concerns with the general description of the transformers. Further it provils informatik about systems for sampling oil from transformers with insulating oil – paper system. Another important part is the description of gas chromatography and TRANSPORT X. Description of the most appropriate evaluation methods for assessment of the state of the transformer is included as well.

Promoting constructiveness in online comment sections is an essential step to make the internet a more productive place. On online marketplaces, customers often have the opportunity to voice their opinion and relate their experience with a given product. In this thesis, we investigate the possibility to model constructiveness in product review in order to promote the most informative and argumentative customer feedback. We develop a new constructiveness 4-class scale taxonomy based on heuristics and specific categorical criteria. We use this taxonomy to annotate 4000 Amazon customer reviews as our training set, referred to as the Corpus for Review Constructiveness (CRC). In addition to the 4-class constructiveness tag, we include a binary tag to compare modeling performance with previous work. We train and test several computational models such as Bidirectional Encoder Representations from Transformers (BERT), a Stacked Bidirectional LSTM and a Gradient Boosting Machine. We demonstrate our annotation scheme’s reliability with a set of inter-annotator agreement experiments, and show that good levels of performance can be reached in both multiclass setting (0.69 F1 and 57% error reduction over the baseline) and binary setting (0.85 F1 and 71% error reduction). Different features are evaluated individually and in combination. Moreover, we compare the advantages, downsides and performance of both feature-based and neural network models. Finally, these models trained on CRC are tested on out-of-domain data (news article comments) and shown to be nearly as proficient as on in-domain data. This work allows the extension of constuctiveness modeling to a new type of data and provides a new non-binary taxonomy for data labeling.

This project explores the usability of Transformers for learning a model that canplay the drums and accompany a human pianist. Building upon previous workusing fuzzy logic systems three experiments are devised to test the usabilityof Transformers. The report also includes a brief survey of algorithmic musicgeneration.The result of the project are that in their current form Transformers cannoteasily learn collaborative music generation. The key insights is that a new wayto encode sequences are needed for collaboration between human and robot inthe music domain. This encoding should be able to handle the varied demandsand lengths of di erent musical instruments.

Financial reports include a lot of useful information for investors, but extracting this information is time-consuming. We think text summarization is a feasible method. In this thesis, we implement BERTSUM, a state-of-the-art language model for text summarization, and evaluate the results by ROUGE metrics. The experiment was carried out on a novel and large-scale financial dataset called EDGAR-CORPUS. The BERTSUM with a transformer achieves the best performance with a ROUGE-L F1 score of 9.26%. We also hand-picked some model-generated summaries that contained common errors and investigated the causes. The results were then compared to previous research. The ROUGE-L F1 value in the previous study was much higher than ours, we think this is due to the length of the financial reports.

Researchers writing scientific articles summarize their work in the abstracts mentioning the final outcome of their study. Argumentation mining can be used to extract the claim of the researchers as well as the evidence that could support their claim. The rapid growth of scientific articles demands automated tools that could help in the detection and evaluation of the scientific claims’ veracity. However, there are neither a lot of studies focusing on claim identification and verification neither a lot of annotated corpora available to effectively train deep learning models. For this reason, we annotated two argument mining corpora and perform several experiments with state-of-the-art BERT-based models aiming to identify and verify scientific claims. We find that using SciBERT provides optimal results regardless of the dataset. Furthermore, increasing the amount of training data can improve the performance of every model we used. These findings highlight the need for large-scale argument mining corpora, as well as domain-specific pre-trained models.

Installation and utilization of residential distribution transformers has not changed substantially over a long period of time. Utilities typically size their transformers based on a formula that takes into account broadly what types and how many dwellings will be connected. Most new residential dwellings feature 200 Amp service per household with an anticipated energy demand of under 20,000 kWh per year. Average electrical energy consumption varies from state to state but averages to 11,280 kWh per year. Energy demand is expected to fall into a typical residential load curve that shows increased demand early in the morning, then decreasing during the day and another peak early to late evening. Distribution transformers are sized at the limit of the combined evening peak with the assumption that the transformer has enough thermal mass to absorb short overloads that may occur when concurrent loading situations among multiple dwellings arise. The assumption that concurrent loading is of short duration and the transformer can cool off during the night time has been validated over the years and has become standard practice. This has worked well when dwelling loads follow an averaging scheme and low level of coincidence. With the arrival of electric vehicles (EV's) this assumption has to be reevaluated. The acquisition of an electric vehicle in a household can drive up energy demand by over 4000 kWh per year. Potentially problematic is the increased capacity of battery packs and the resulting proliferation of Level 2 chargers. The additional load of a single Level 2 charger concurring with the combined evening peak load will push even conservatively sized distribution transformers over their nameplate rating for a substantial amount of time. Additionally, unlike common household appliances of similar power requirements such as ovens or water heaters, a Level 2 battery charger will run at peak power consumption for several hours, and the current drawn by the EVs has very high levels of harmonic distortion. The excessive loading and harmonic profile can potentially result in damaging heat build-up resulting in asset degradation. In this thesis I present a device and method that monitors pole mounted distribution transformers for overheating, collect and wirelessly upload data and initiate commands to chargers to change output levels from Level 2 to Level 1 or shut down EV charging altogether until the transformer returns into safe operational range.