Furthering Service 4.0: Harnessing Intelligent Immersive Environments and Systems

With the increasing complexity of service operations in different industries and more advanced uses of specialized equipment and procedures, the great current challenge for companies is to increase employees' expertise and their ability to maintain and improve service quality. In this regard, Service 4.0 aims to support and promote innovation in service operations using emergent technology. Current technological innovations present a significant opportunity to provide on-site, real-time support for field service professionals in many areas

Artificial Intelligence and Deep Reinforcement Learning Stock Market Predictions

Billions of dollars are traded automatically in the stock market every day, including algorithms that use artificial intelligence (AI) techniques, but there are still questions regarding how AI trades successfully. The black box nature of these AI techniques, namely neural networks, gives pause to entrusting it with valuable trading funds. This dissertation applies AI techniques to stock market trading strategies, but it also provides exploratory research into how these techniques predict the stock market successfully. This dissertation presents the work of three research papers. The first paper presented in this dissertation applies a artificial intelligence technique, reinforcement learning, to candlestick pattern trading. This paper also analyzes how the DDQN trades, through the use of a more recent technique, feature map visualizations. The second and third paper analyze AI techniques in a pairs trading strategy. The first paper results show that the DDQN is able to outperform the S&P 500 Index returns. Results also show that the CNN is able to switch its attention from all the candles in a candlestick image to the more recent candles in the image, based on an event such as the coronavirus stock market crash of 2020.The second paper results show fuzzy logic applied to pairs trading strategy for 22 stock pairs, increases annual returns on average from 15% to 17%. The third paper results show a DDQN was able to accurately predict the spread of the Adobe/Red Hat pair, for positive returns. This dissertation shows that AI techniques are successful in predicting the stock market, but more importantly it provides research tools and methods to better understand and implement these techniques in stock market trading

Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms

Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms

Automated model for the sustainability of the educational physical infrastructure in Smart Cities

Sustainability requires implementing strategies with actions that generate solutions for good living; being fundamental the development and evaluation of models and / or automated prototypes for their implantation in the institutions of the smart cities. Higher and postgraduate educational institutions are responsible for research, deepening attention to social problems to offer solutions that impact social, environmental and economic development. A strategic solution is the automation of the physical infrastructure, promoting the construction of automated models, with home automation, sustainable architecture, among other elements of engineering and technological innovation with a focus on environmental studies. The study contemplated mixed and applied research. The space design modeled with electronic resources, sustainable architecture, home automation engineering and software was presented and evaluated. It was proposed as a sustainable development strategy for its implementation in a physical infrastructure of the Technological Institute of Acapulco dependent on the National Technology of Mexico. The evaluation of the automated model determined the viability of implementing the prototype in the physical infrastructure as a feasible and sustainable strategic model for educational institutions in smart cities. Among the conclusions, the need to design technological development models and strategies for energy saving and resource safety for educational institutions in smart cities, which offer sustainable development alternatives focused on caring for the environment, is reflected., as a proposal for a sustainable model with the possibility of impacting technological development and engaging in multidisciplinary research on environmental issues

Explainable Neural Networks based Anomaly Detection for Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are the core of modern critical infrastructure (e.g. power-grids) and securing them is of paramount importance. Anomaly detection in data is crucial for CPS security. While Artificial Neural Networks (ANNs) are strong candidates for the task, they are seldom deployed in safety-critical domains due to the perception that ANNs are black-boxes. Therefore, to leverage ANNs in CPSs, cracking open the black box through explanation is essential. The main objective of this dissertation is developing explainable ANN-based Anomaly Detection Systems for Cyber-Physical Systems (CP-ADS). The main objective was broken down into three sub-objectives: 1) Identifying key-requirements that an explainable CP-ADS should satisfy, 2) Developing supervised ANN-based explainable CP-ADSs, 3) Developing unsupervised ANN-based explainable CP-ADSs. In achieving those objectives, this dissertation provides the following contributions: 1) a set of key-requirements that an explainable CP-ADS should satisfy, 2) a methodology for deriving summaries of the knowledge of a trained supervised CP-ADS, 3) a methodology for validating derived summaries, 4) an unsupervised neural network methodology for learning cyber-physical (CP) behavior, 5) a methodology for visually and linguistically explaining the learned CP behavior. All the methods were implemented on real-world and benchmark datasets. The set of key-requirements presented in the first contribution was used to evaluate the performance of the presented methods. The successes and limitations of the presented methods were identified. Furthermore, steps that can be taken to overcome the limitations were proposed. Therefore, this dissertation takes several necessary steps toward developing explainable ANN-based CP-ADS and serves as a framework that can be expanded to develop trustworthy ANN-based CP-ADSs

Application of Advanced Early Warning Systems with Adaptive Protection

This project developed and field-tested two methods of Adaptive Protection systems utilizing synchrophasor data. One method detects conditions of system stress that can lead to unintended relay operation, and initiates a supervisory signal to modify relay response in real time to avoid false trips. The second method detects the possibility of false trips of impedance relays as stable system swings “encroach” on the relays’ impedance zones, and produces an early warning so that relay engineers can re-evaluate relay settings. In addition, real-time synchrophasor data produced by this project was used to develop advanced visualization techniques for display of synchrophasor data to utility operators and engineers