Stability analysis of switched dc-dc boost converters for integrated circuits

Boost converters are very important circuits for modern devices, especially battery- operated integrated circuits. This type of converter allows for small voltages, such as those provided by a battery, to be converted into larger voltage more suitable for driving integrated circuits. Two regions of operation are explored known as Continuous Conduction Mode and Discontinuous Conduction Mode. Each region is analyzed in terms of DC and small-signal performance. Control issues with each are compared and various error amplifier architectures explored. A method to optimize these amplifier architectures is also explored by means of Genetic Algorithms and Particle Swarm Optimization. Finally, stability measurement techniques for boost converters are explored and compared in order to gauge the viability of each method. The Middlebrook Method for measuring stability and cross-correlation are explored here

Network-on-Chip

Addresses the Challenges Associated with System-on-Chip Integration Network-on-Chip: The Next Generation of System-on-Chip Integration examines the current issues restricting chip-on-chip communication efficiency, and explores Network-on-chip (NoC), a promising alternative that equips designers with the capability to produce a scalable, reusable, and high-performance communication backbone by allowing for the integration of a large number of cores on a single system-on-chip (SoC). This book provides a basic overview of topics associated with NoC-based design: communication infrastructure design, communication methodology, evaluation framework, and mapping of applications onto NoC. It details the design and evaluation of different proposed NoC structures, low-power techniques, signal integrity and reliability issues, application mapping, testing, and future trends. Utilizing examples of chips that have been implemented in industry and academia, this text presents the full architectural design of components verified through implementation in industrial CAD tools. It describes NoC research and developments, incorporates theoretical proofs strengthening the analysis procedures, and includes algorithms used in NoC design and synthesis. In addition, it considers other upcoming NoC issues, such as low-power NoC design, signal integrity issues, NoC testing, reconfiguration, synthesis, and 3-D NoC design. This text comprises 12 chapters and covers: The evolution of NoC from SoC—its research and developmental challenges NoC protocols, elaborating flow control, available network topologies, routing mechanisms, fault tolerance, quality-of-service support, and the design of network interfaces The router design strategies followed in NoCs The evaluation mechanism of NoC architectures The application mapping strategies followed in NoCs Low-power design techniques specifically followed in NoCs The signal integrity and reliability issues of NoC The details of NoC testing strategies reported so far The problem of synthesizing application-specific NoCs Reconfigurable NoC design issues Direction of future research and development in the field of NoC Network-on-Chip: The Next Generation of System-on-Chip Integration covers the basic topics, technology, and future trends relevant to NoC-based design, and can be used by engineers, students, and researchers and other industry professionals interested in computer architecture, embedded systems, and parallel/distributed systems

Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate

The utilization of artificial intelligence (AI) has become imperative across various domains, including the oil and gas industry, which covers several fields, including reservoirs, drilling, and production. In oil and gas production, conventional methods, such as reservoir simulation, are used to predict the oil production rate. This simulation requires comprehensive data, so each process step takes a long time and is expensive. AI is urgently needed and can be a solution in this case. This research aims to apply AI techniques to forecast oil production rates based on water injection rates from two injection wells. Three wells are connected with a direct line drive pattern. Three different AI methods were applied, including multiple linear polynomial regression (PR), multiple linear regression (MLR), and artificial neural networks (ANN) in constructing oil production rate prediction models. Actual field data of 1180 data are used, including water injection rate data from two injection wells and oil production history data from one production well. The dataset has been split randomly into 80% training and 20% allocated for testing subsets. The training data is used to build predictive models, while the testing data is used to validate model performance. Comparative analysis selects the model with the lowest root mean square error (RMSE) and the highest R^2 test value. Results demonstrate that the ANN model achieves the smallest Root Mean Square Error (RMSE) of 0.142 and the highest R^2 test value of 16.2%, outperforming the PR and MLR methods. The ANN prediction model provides a rapid and efficient approach to estimating oil production rates

Testing of leakage current failure in ASIC devices exposed to total ionizing dose environment using design for testability techniques

Due to the advancements in technology, electronic devices have been relied upon to operate under harsh conditions. Radiation is one of the main causes of different failures of the electronics devices. According to the operation environment, the sources of the radiation can be terrestrial or extra-terrestrial. For terrestrial the devices can be used in nuclear reactors or biomedical devices where the radiation is man-made. While for the extra- terrestrial, the devices can be used in satellites, the international space station or spaceships, where the radiation comes from various sources like the Sun. According to the operation environment the effects of radiation differ. These effects falls under two categories, total ionizing dose effect (TID) and single event effects (SEEs). TID effects can be affect the delay and leakage current of CMOS circuits negatively. The affects can therefore hinder the integrated circuits\u27 operation. Before the circuits are used, particularly in critical radiation heavy applications like military and space, testing under radiation must be done to avoid any failures during operation. The standard in testing electronic devices is generating worst case test vectors (WCTVs) and under radiation using these vectors the circuits are tested. However, the generation of these WCTVs have been very challenging so this approach is rarely used for TIDs effects. Design for testability (DFT) have been widely used in the industry for digital circuits testing applications. DFT is usually used with automatic test patterns generation software to generate test vectors against fault models of manufacturer defects for application specific integrated circuit (ASIC.) However, it was never used to generate test vectors for leakage current testing induced in ASICs exposed to TID radiation environment. The purpose of the thesis is to use DFT to identify WCTVs for leakage current failures in sequential circuits for ASIC devices exposed to TID. A novel methodology was devised to identify these test vectors. The methodology is validated and compared to previous non DFT methods. The methodology is proven to overcome the limitation of previous methodologies

ne–xt facades: Proceedings of the COST Action TU1403 Adaptive Facades Network Mid-term Conference

The ne-xt facades conference is the official International Mid-term Conference of the European COST Action TU1403 ‘Adaptive Facades Network’, an international scientific cooperation with the aim to harmonise, share and disseminate technological knowledge on adaptive facades on the European level. During the mid-term conference first results are presented to stakeholders from industry and design and to the public. The goal is to share knowledge and discuss novel facade concepts, effective evaluation tools and design methods for adaptive facades. Alongside the contributions from members of the COST Action, the conference received many contributions from external researchers and the industry. This added to the interesting debate about adaptive facades we believe it was an excellent stage to test the first results of the COST Action

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable