Behavioral Modeling Paradigm for DC Nanogrid Based Distributed Energy Systems

The remarkable progress of power electronic converters (PEC) technology has led to their increased penetration in distributed energy systems (DES). Particularly, the direct current (dc) nanogrid-based DES embody a variety of sources and loads, connected through a central dc bus. Therefore, PECs are required to be employed as an interface. It would facilitate incorporation of the renewable energy sources and battery storage system into dc nanogrids. However, it is more challenging as the integration of multiple modules may cause instabilities in the overall system dynamics. Future dc nanogrids are envisioned to deploy ready-to-use commercial PEC, for which designers have no insight into their dynamic behavior. Furthermore, the high variability of the operating conditions constitute a new paradigm in dc nanogrids. It exacerbates the dynamic analysis using traditional techniques. Therefore, the current work proposes behavioral modeling to perform system level analysis of a dc nanogrid-based DES. It relies only on the data acquired via measurements performed at the input–output terminals only. To verify the accuracy of the model, large signal disturbances are applied. The matching of results for the switch model and its behavioral model verifies the effectiveness of the proposed model.

Towards a Conceptual Framework of Comprehensive Video Game Player Profiles: Player Models, Mental Models, and Behavior Models

Current approaches to player profiling are limited in that they typically employ only a single one of numerous of available techniques shown to have utility for categorizing and explaining player behavior. We propose a more comprehensive Video Game Player Profile Framework that considers the demographic, psychographic, mental model, and behavioral modeling approaches shown to be effective for describing gamer populations. We suggest that our proposed approach can improve the efficacy of video game player profiles by grounding data-driven techniques in game analytics with the theoretical backing of demographic, psychometric, and psychographic measurements. We provide an overview of our proposed framework, discuss the usage and relevance of each component technique, and provide a proof-of-concept demonstration with archived data.

Behavioral Modeling Paradigm for More Electric Aircraft Power Electronic Converters

To control the power flow among various energy sources and loads of a power system of modern more electric aircrafts, power electronics converters are employed. The integration of multiple sources into distribution system and their interconnection with variety of loads through power electronic converters results in a complex dynamic system. Modeling of these systems prior to implementation becomes necessary to analyze and predict system’s behavior. The classical modeling approaches require detail knowledge about the topology and parameters of the active and passive components of the power electronics converters. While in modern system, most of the power electronics converters are ready to use power electronics modules. These modules come from different manufacturers, lacking the necessary information to build the conventional switch or average models. The chapter would cover dynamic behavioral modeling technique for power electronics systems to be employed in more electric aircrafts, which do not require any prior information about the internal details of the system.

Upgrading Behavioral Models for the Design of Digital Predistorters

This work presents a strategy to upgrade models for power amplifier (PA) behavioral modeling and digital predistortion (DPD). These incomplete structures are the consequence of nonlinear order and memory depth model truncation with the purpose of reducing the demand of the limited computational resources available in standard processors. On the other hand, the alternative use of model structures pruned a priori does not guarantee that every significant term is included. To improve the limited performance of an incomplete model, a general procedure to augment its structure by incorporating significant terms is demonstrated. The sparse nature of the problem allows a successive search incorporating additional terms with higher nonlinear order and memory depth. This approach is investigated in the modeling and linearization of a commercial class AB PA operating at a compression point of about 6 dB, and a class J PA operating near saturation. Results highlight the capabilities of this upgrading procedure in the improvement of linearization capabilities of DPDs.

Radon Awareness and Policy Perspectives on Testing and Mitigation

One in three homes in Utah (USA) contains dangerous levels of radon. Except for a radon mitigation certification law, Utah’s radon laws are nonexistent. To determine public perception of state policies on radon testing and mitigation, a social cognitive theory-based 52-item questionnaire was administered to residents (N = 307) who visited the Utah County Health Department (UCHD) during the study period. Respondents were divided into an Environmental Health Group (n = 110), who purchased a radon kit, and Vital Records Control Group (n = 197), who filed/obtained birth/death certificates at UCHD. Ninety percent responded they had never tested their homes for radon, and 99% were not aware of state policies regarding radon. Support for various radon policies was significantly associated with older age (odds ratios (OR): 0.37–0.52), being female (OR: 2.60–7.79), lower annual family income (OR: 2.27), and theoretical constructs of behavioral modeling (OR: 2.31–2.55) and risk perception (OR: 2.55–3.71). To increase awareness, testing, and remediation, respondents suggested increasing public education/awareness, requiring testing in homes, businesses, and public buildings, and increasing access to testing. Multi-sectoral radon risk reduction programs could incorporate behavioral modeling and risk perception as components to create a radon testing and mitigation culture in Utah.