Feasibility study of an Integrated Program for Aerospace-vehicle Design (IPAD) system. Volume 2: Characterization of the IPAD system, phase 1, task 1

The aircraft design process is discussed along with the degree of participation of the various engineering disciplines considered in this feasibility study

Real-time Energy Management in Smart Homes through Deep Reinforcement Learning

In light of the growing prevalence of distributed energy resources, energy storage systems (ESs), and electric vehicles (EVs) at the residential scale, home energy management (HEM) systems have become instrumental in amplifying economic advantages for consumers. These systems traditionally prioritize curtailing active power consumption, often at an expense of overlooking reactive power. A significant imbalance between active and reactive power can detrimentally impact the power factor in the home-to-grid interface. This research presents an innovative strategy designed to optimize the performance of HEM systems, ensuring they not only meet financial and operational goals but also enhance the power factor. The approach involves the strategic operation of flexible loads, meticulous control of thermostatic load in line with user preferences, and precise determination of active and reactive power values for both ES and EV. This optimizes cost savings and augments the power factor. Recognizing the uncertainties in user behaviors, renewable energy generations, and external temperature fluctuations, our model employs a Markov decision process for depiction. Moreover, the research advances a model-free HEM system grounded in deep reinforcement learning, thereby offering a notable proficiency in handling the multifaceted nature of smart home settings and ensuring real-time optimal load scheduling. Comprehensive assessments using real-world datasets validate our approach. Notably, the proposed methodology can elevate the power factor from 0.44 to 0.9 and achieve a significant 31.5% reduction in electricity bills, while upholding consumer satisfaction

Management model for energy efficiency - Intelligent System module

The power consumption in buildings represents a 30-40% of the final energy usage, hence it is necessary to minimize the power consumption by optimizing the operation of several loads without impacting in the customer’s comfort. According to the above in this work an intelligent approach framing in a management model is presented for the power consumption management of devices taking into account some variables as indoor temperature, outdoor temperature, illuminance and presence. Furthermore, in this research the integration of several Demand Side Management (DSM) criteria with one criterion based on neural networks and other inspired on differential tariff is carried out through dynamic and intelligent selections according to variables performance and customer´s preferences, e.g. priority list of criteria, operation based on comfort or consumption, in addition to other preferences as temperature. Likewise, a previous diagnosis analysis through energy audit is carried out to evaluate devices performance and customer habits. Experimental testing to the proposed approach has been performed in an environment object of study with the consumption data base and its performance tested in simulations runs. The testing results show that energy savings can be achieved through of recommendations provided by energy audit and proposed states by dynamic manager.MaestríaMagister en Ingeniería Electrónic

Book of abstracts of the 2nd International Conference of TEMA: mobilizing projects

Based on its Human Capital and Capacities, the Centre for Mechanical Technology and Automation (TEMA) embraces a mission aiming to contribute to a sustainable industry, with specially focus on the surrounding SMEs, and to the wellbeing of society. Sustainable manufacturing aims to contribute to the development of a sustainable industry by developments and innovations on manufacturing engineering and technologies, to increase productivity, improve products quality and reduce waste in production processes. Technologies for the Wellbeing wishes to contribute to the wellbeing of society by the development of supportive engineering systems focusing on people and their needs and intending to improve their quality of life. TEMA intends to maximize its national and international impact in terms of scientific productivity and its transfer to society by tackling the relevant challenges of our time. TEMA is aware of the major challenges of our days, not only confined to scientific issues but also the societal ones, (a strategic pillar of the Horizon 2020 program), at the same time placing an effort to have its research disseminated, in high impact journals to the international scientific community. (...)publishe

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 133)

This special bibliography lists 276 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in September 1974

Techno-economic assessment of thermal energy storage technologies for demand-side management in low-temperature individual heating systems

The combined use of\ua0thermal energy storage\ua0(TES) technologies and\ua0heat pumps\ua0in building energy systems has been approved to achieve demand-side management. Although there is an increasing number of case studies about the TES applications, crosswise techno-economic evaluations of different technologies are rare, especially for applications in individual heating systems where the storage temperature range is less than 20\ua0K. Hence, in this study, three TES options; water tank (WT),\ua0phase change material\ua0tank, and\ua0building thermal\ua0mass (BTM) are simulated and compared. A\ua0systematic analysis\ua0approach was proposed to assure impartial comparisons of the energy performance and the life-cycle costs (LCC). Special focus was paid on practical issues such as restricted charging power for different TES technologies. It was found that the majority of LCC savings arises from the peak load reduction. The study also shows that BTM is the most cost-effective TES technology while the WT is the least attractive option, due to larger heat loss and lower storage density. Moreover, less discharged energy and cost savings were found in well-insulated buildings due to the restricted discharging power. Still, there could be more incentives for household TES technologies if additional prices or policies are implemented

Unveiling AI Aversion: Understanding Antecedents and Task Complexity Effects

Artificial Intelligence (AI) has generated significant interest due to its potential to augment human intelligence. However, user attitudes towards AI are diverse, with some individuals embracing it enthusiastically while others harbor concerns and actively avoid its use. This two essays\u27 dissertation explores the reasons behind user aversion to AI. In the first essay, I develop a concise research model to explain users\u27 AI aversion based on the theory of effective use and the adaptive structuration theory. I then employ an online experiment to test my hypotheses empirically. The multigroup analysis by Structural Equation Modeling shows that users\u27 perceptions of human dissimilarity, AI bias, and social influence strongly drive AI aversion. Moreover, I find a significant difference between the simple and the complex task groups. This study reveals why users avert using AI by systematically examining the factors related to technology, user, task, and environment, thus making a significant contribution to the emerging field of AI aversion research. Next, while trust and distrust have been recognized as influential factors shaping users\u27 attitudes towards IT artifacts, their intricate relationship with task characteristics and their impact on AI aversion remains largely unexplored. In my second essay, I conduct an online randomized controlled experiment on Amazon Mechanical Turk to bridge this critical research gap. My comprehensive analytic approach, including structural equation modeling (SEM), ANOVA, and PROCESS conditional analysis, allowed me to shed light on the intricate web of factors influencing users\u27 AI aversion. I discovered that distrust and trust mediate between task complexity and AI aversion. Moreover, this study unveiled intriguing differences in these mediated relationships between subjective and objective task groups. Specifically, my findings demonstrate that, for objective tasks, task complexity can significantly increase aversion by reducing trust and significantly decrease aversion by reducing distrust. In contrast, for subjective tasks, task complexity only significantly increases aversion by enhancing distrust. By considering various task characteristics and recognizing trust and distrust as vital mediators, my research not only pushes the boundaries of the human-AI literature but also significantly contributes to the field of AI aversion

Enhancement of perceived quality of service for voice over internet protocol systems

Voice over Internet Protocol (WIP) applications are becoming more and more popular in the telecommunication market. Packet switched V61P systems have many technical advantages over conventional Public Switched Telephone Network (PSTN), including its efficient and flexible use of the bandwidth, lower cost and enhanced security. However, due to the IP network's "Best Effort" nature, voice quality are not naturally guaranteed in the VoIP services. In fact, most current Vol]P services can not provide as good a voice quality as PSTN. IP Network impairments such as packet loss, delay and jitter affect perceived speech quality as do application layer impairment factors, such as codec rate and audio features. Current perceived Quality of Service (QoS) methods are mainly designed to be used in a PSTN/TDM environment and their performance in V6IP environment is unknown. It is a challenge to measure perceived speech quality correctly in V61P system and to enhance user perceived speech quality for VoIP system. The main goal of this project is to evaluate the accuracy of the existing ITU-T speech quality measurement method (Perceptual Evaluation of Speech Quality - PESQ) in mobile wireless systems in the context of V61P, and to develop novel and efficient methods to enhance the user perceived speech quality for emerging V61P services especially in mobile V61P environment. The main contributions of the thesis are threefold: (1) A new discovery of PESQ errors in mobile VoIP environment. A detailed investigation of PESQ performance in mobile VoIP environment was undertaken and included setting up a PESQ performance evaluation platform and testing over 1800 mobile-to-mobile and mobileto- PSTN calls over a period of three months. The accuracy issues of PESQ algorithm was investigated and main problems causing inaccurate PESQ score (improper time-alignment in the PESQ algorithm) were discovered . Calibration issues for a safe and proper PESQ testing in mobile environment were also discussed in the thesis. (2) A new, simple-to-use, V611Pjit ter buffer algorithm. This was developed and implemented in a commercial mobile handset. The algorithm, called "Play Late Algorithm", adaptively alters the playout delay inside a speech talkspurt without introducing unnecessary extra end-to-end delay. It can be used as a front-end to conventional static or adaptive jitter buffer algorithms to provide improved performance. Results show that the proposed algorithm can increase user perceived quality without consuming too much processing power when tested in live wireless VbIP networks. (3) A new QoS enhancement scheme. The new scheme combines the strengths of adaptive codec bit rate (i. e. AMR 8-modes bit rate) and speech priority marking (i. e. giving high priority for the beginning of a voiced segment). The results gathered on a simulation and emulation test platform shows that the combined method provides a better user perceived speech quality than separate adaptive sender bit rate or packet priority marking methods