Model migration neural network for predicting battery aging trajectories

Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction

Interactive Exploration of Multitask Dependency Networks

Scientists increasingly depend on machine learning algorithms to discover patterns in complex data. Two examples addressed in this dissertation are identifying how information sharing among regions of the brain develops due to learning; and, learning dependency networks of blood proteins associated with cancer. Dependency networks, or graphical models, are learned from the observed data in order to make comparisons between the sub-populations of the dataset. Rarely is there sufficient data to infer robust individual networks for each sub-population. The multiple networks must be considered simultaneously; exploding the hypothesis space of the learning problem. Exploring this complex solution space requires input from the domain scientist to refine the objective function. This dissertation introduces a framework to incorporate domain knowledge in transfer learning to facilitate the exploration of solutions. The framework is a generalization of existing algorithms for multiple network structure identification. Solutions produced with human input narrow down the variance of solutions to those that answer questions of interest to domain scientists. Patterns, such as identifying differences between networks, are learned with higher confidence using transfer learning than through the standard method of bootstrapping. Transfer learning may be the ideal method for making comparisons among dependency networks, whether looking for similarities or differences. Domain knowledge input and visualization of solutions are combined in an interactive tool that enables domain scientists to explore the space of solutions efficiently

Development of detailed prime mover models and distributed generation for an on-board naval power system trainer

2012 Summer.Includes bibliographical references.A power management platform (PMP) has been developed for an electric generation plant on-board a U.S. naval ship. The control hardware and software interface with a Human Machine Interface (HMI) where the sailor can monitor and control the electric plant state. With the implementation of the PMP, there becomes a need to train the sailors how to effectively use the HMI to manage the power plant. A power system trainer was developed with all the physical parts of the power system modeled in software that communicate to the control software, HMI software, and training software. Previous simulation models of the prime movers created in MATLAB® Simulink® (developed at Woodward, Inc. for control code testing purposes) were inadequate to simulate all the signals the control software receives. Therefore, the goal of this research was to increase the accuracy and detail of the existing prime mover models and add detail to the current electrical grid model for use in a power system trainer while maintaining real-time simulation. This thesis provides an overview encompassing techniques used to model various prime movers, auxiliary systems, and electrical power system grids collected through literary research as well as creative adaptation. For the prime movers, a mean value model (MVM) was developed for the diesel engine as well as a thermodynamic based steam turbine model. A heat transfer model was constructed for an AC synchronous electrical generator with a Totally Enclosed Air to Water Cooled (TEWAC) cooling arrangement. A modular heat exchanger model was implemented and the electrical grid model was expanded to cover all of the electrical elements. Models now dynamically simulate all the hardware signals in software and the training simulation executes in real-time

Optimization of highway work zone decisions considering Short-term and Long-term Impacts

With the increase of the number, duration, and scope of maintenance projects on the national highway system, transportation agencies face great challenges in developing effective comprehensive work zone management plans which minimize the negative impacts on road users and workers. The types of maintenance operation, timing, duration, configuration, and user impact mitigation strategies are major considerations in developing work zone management plans. Some of those decisions may not only affect road users during the maintenance phase but also have significant impacts on pavement serviceability in future years. This dissertation proposes a systematic methodology for jointly optimizing critical work zone decisions, based on analytical and simulation models developed to estimate short-term impacts during the maintenance periods and long-term impacts over the pavement life cycle. The dissertation starts by modeling the effects of different work zone decisions on agency and user costs during the maintenance phase. An analytic one-time work zone cost model is then formulated based on simulation analysis results. Next, a short-term work zone decision optimization model is developed to find the best combination of lane closure and traffic control strategies which can minimize the one-time work zone cost. Considering the complex and combinatorial nature of this optimization problem, a heuristic optimization algorithm, named two-stage modified population-based simulated annealing (2PBSA), is designed to search for a near-optimal solution. For those maintenance projects that may need more detailed estimation of user delay or other impacts, a simulation-based optimization method is proposed in this study. Through a hybrid approach combining simulation and analytic methods along with parallel computing techniques, the proposed method can yield satisfactory solutions while reducing computational efforts to a more acceptable level. The last part of this study establishes a framework for jointly optimizing short-term and long-term work zone decisions with the objective of maximizing cost-effectiveness. Case studies are conducted to test the performance of the proposed methods and develop guidelines for development of work zone management plans

Towards Zero Touch Next Generation Network Management

The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the front and mid-haul and backbone networking segments servicing them. One of the main changes made was virtualizing the networking components to allow for faster deployment and reconfiguration when needed. However, adopting such technologies poses several challenges, such as improving the performance and efficiency of these systems by properly orchestrating the services to the ideal edge device. A second challenge is ensuring the backbone optical networking maximizes and maintains the throughput levels under more dynamically variant conditions. A third challenge is addressing the limitation of placement techniques in O-RAN. In this thesis, we propose using various optimization modeling and machine learning techniques in three segments of network systems towards lowering the need for human intervention targeting zero-touch networking. In particular, the first part of the thesis applies optimization modeling, heuristics, and segmentation to improve the locally driven orchestration techniques, which are used to place demands on edge devices throughput to ensure efficient and resilient placement decisions. The second part of the thesis proposes using reinforcement learning (RL) techniques on a nodal base to address the dynamic nature of demands within an optical networking paradigm. The RL techniques ensure blocking rates are kept to a minimum by tailoring the agents’ behavior based on each node\u27s demand intake throughout the day. The third part of the thesis proposes using transfer learning augmented reinforcement learning to drive a network slicing-based solution in O-RAN to address the stringent and divergent demands of 5G applications. The main contributions of the thesis consist of three broad parts. The first is developing optimal and heuristic orchestration algorithms that improve demands’ performance and reliability in an edge computing environment. The second is using reinforcement learning to determine the appropriate spectral placement for demands within isolated optical paths, ensuring lower fragmentation and better throughput utilization. The third is developing a heuristic controlled transfer learning augmented reinforcement learning network slicing in an O-RAN environment. Hence, ensuring improved reliability while maintaining lower complexity than traditional placement techniques

Model reaplikacije za planiranje, razvoj i integraciju pametnih centraliziranih toplinskih sustava u pametne energetske sustave

U ovom radu obrađena je tema reaplikacije pametnih centraliziranih toplinskih sustava kao dijela pametnih energetskih sustava u pametnim gradovima. Rad je napravljen u skladu s zadacima postavljenima u radnom paketu 8, projekta SmartEnCity. Najprije je proveden sažeti pregled projekata pametnih gradova i pametnih centraliziranih toplinskih sustava kako bi se odredili važni podaci iz projekata u tijeku. Dobiveni podaci će služiti kao vrijedan doprinos u SmartEnCity projektu. Opisom pametnih centraliziranih toplinskih sustava su definirane njihove glavne karakteristike. Opis je dan uz pomoć sveobuhvatnog pregleda postojeće literature. Kako bi se pametni centralizirani toplinski sustav kopirao, tj. replicirao iz jednog grada u drugi, potrebno je odrediti važne aspekte za replikaciju. Glavni aspekti koji su definirani i objašnjeni su: geografski, financijski, tehnički i aspekt vlasništva. Nakon definiranja važnih aspekata za replikaciju, definirani su i opisani važni koraci metodologije za replikaciju pametnih centraliziranih toplinskih sustava, te je izrađen dijagram toka replikacije dajući grafički prikaz metodologije, korak po korak. Konačno, definirana su i opisana četiri alata za korištenje u svrhu replikacije aktivnosti u gradu Sønderborg. Također, predložen je i sadržaj plana djelovanja za individualnu replikaciju u gradu Sønderborg