Integrated Application of Active Controls (IAAC) technology to an advanced subsonic transport project. ACT/Control/Guidance System study. Volume 2: Appendices

The integrated application of active controls (IAAC) technology to an advanced subsonic transport is reported. Supplementary technical data on the following topics are included: (1) 1990's avionics technology assessment; (2) function criticality assessment; (3) flight deck system for total control and functional features list; (4) criticality and reliability assessment of units; (5) crew procedural function task analysis; and (6) recommendations for simulation mechanization

Deep Reinforcement Learning for the Optimization of Building Energy Control and Management

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). We assume that each building in our campus is equipped with smart meter and communication system which is envisioned in the future smart grid. For academic and commercial buildings, HVAC systems consume considerable electrical energy and impact the personnels in the buildings which is interpreted as monetary value in this article. Therefore, we define social cost as the combination of energy expense and cost of human working productivity reduction. We implement game theory and formulate a controlling and scheduling game for HVAC system, where the players are the building managers and their strategies are the indoor temperature settings for the corresponding building. We use the University of Denver campus power system as the demonstration smart grid and it is assumed that the utility company can adopt the real-time pricing mechanism, which is demonstrated in this paper, to reflect the energy usage and power system condition in real time. For general scenarios, the global optimal results in terms of minimizing social costs can be reached at the Nash equilibrium of the formulated objective function. The proposed distributed HVAC controlling system requires each manager set the indoor temperature to the best response strategy to optimize their overall management. The building managers will be willing to participate in the proposed game to save energy cost while maintaining the indoor in comfortable zone. With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed. The combination of deep neural network and reinforcement learning rockets up the research of deep reinforcement learning, and this manuscript contributes to the research of power energy management by developing and implementing the deep reinforcement learning to control the HVAC systems in distribution power system. Simulation results prove that the proposed methodology can set the indoor temperature with respect to real-time pricing and the number of inside occupants, maintain indoor comfort, reduce individual building energy cost and the overall campus electricity charges. Compared with the traditional game theoretical methodology, the RL based gaming methodology can achieve the optiaml resutls much more quicker

KD-ACP: A Software Framework for Social Computing in Emergency Management

This paper addresses the application of a computational theory and related techniques for studying emergency management in social computing. We propose a novel software framework called KD-ACP. The framework provides a systematic and automatic platform for scientists to study the emergency management problems in three aspects: modelling the society in emergency scenario as the artificial society; investigating the emergency management problems by the repeat computational experiments; parallel execution between artificial society and the actual society managed by the decisions from computational experiments. The software framework is composed of a series of tools. These tools are categorized into three parts corresponding to “A,” “C,” and “P,” respectively. Using H1N1 epidemic in Beijing city as the case study, the modelling and data generating of Beijing city, experiments with settings of H1N1, and intervention measures and parallel execution by situation tool are implemented by KD-ACP. The results output by the software framework shows that the emergency response decisions can be tested to find a more optimal one through the computational experiments. In the end, the advantages of the KD-ACP and the future work are summarized in the conclusion

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

Co-benefits of urban climate action: a framework for cities

Why do climate co-benefits matter for cities? • The evidence suggests that citizens are more likely to take action on climate change, or more likely to support governments that take action on climate change, if the wider co-benefits of those actions are emphasised. • At the same time, policies that are aimed at supporting innovation, delivering economic benefits and enhancing the quality of life of citizens can potentially lead to major climate cobenefits (e.g. reduced greenhouse gas emissions) which would be more challenging to achieve if climate action were the primary objective. • At the city level, the potential of co-benefits is particularly great as citizens can often witness the results of policy actions more directly on their daily lives. Definition and taxonomy of co-benefits • The term co-benefits has a wide range of definitions in the climate literature, with over 20 terms identified in the literature that are used synonymously or in a similar context. • The term co-benefits varies in intentionality (e.g. is climate the primary or secondary objective, or simply an unintentional benefit?), scope (e.g. does it include mitigation benefits, adaptation benefits or both?), and scale (e.g. are the benefits short term and local, or long term and global?). • Co-benefits may be (1) secondary benefits from climate policy action, (2) secondary climate benefits from other policy actions, or (3) the combination of climate and non-climate benefits; both of which are targeted under an integrated policy programme. • The wide range of established definitions of co-benefits used by authoritative organisations means that formulating a taxonomy of co-benefits with broad buy-in from policy makers is challenging. Results of literature review • Health, Land Use and Transport were the top three sectors for the number of co-benefits, with over 40 co-benefits identified in each. • Waste, Air Quality, Transport and Energy had particularly high numbers of mitigation cobenefits in the literature reviewed. Adaptation co-benefits were particularly strong for Disaster and Emergency, Food Security and Tourism, Culture and Sport. Land Use, Health, Water and Education tended to be strong for both mitigation and adaptation co-benefits

A Modelling Study of Road Traffic Contributions to Ambient PM2.5 Concentrations in Lagos

As the fastest growing city in Africa, Lagos experiences extremely high levels of air pollution. While there are many sources of air pollution in Lagos, road traffic has been widely reported as the most prominent. Due to a dearth of studies on modelling of pollutant dispersions from vehicular emissions, this study adapted the OSCAR System to model the contributions of road traffic to ambient concentrations of PM2.5 in the megacity. The model was evaluated by comparing its predicted PM2.5 concentrations with the observed concentrations in the study area. This comparison was carried out using a number of conventional statistical parameters: model bias, normalised mean square error, fractional bias, correlation coefficient (R) and factor of 2 analysis (F2). The evaluation showed aggregate R and F2 values of 0.66 and 0.80 respectively. This implies a good level of agreement between the measured and the predicted PM2.5 concentrations. For November 2018, the model predicted mean traffic increment of 28.1µg/m3 (37.2%) - 29.3 µg/m3 (38.2%) along the Mile 12 – Ikorodu road. However, the predicted increment around the Expressway (a busier road) was 36.5 µg/m3 (43.5%). The Ikorodu -Mile 12 road is a very important traffic corridor in the Lagos Metropolitan Area – being the pioneering route for the government’s Bus Rapid Transit scheme. A scenario analysis carried out in this study shows that under a fixed meteorological condition, traffic contributions (to ambient concentrations of PM2.5) would increase by a factor of 7 (from November 2010 to November 2018) near the Ikorodu road. Further, it reveals that cars are the highest emitters of PM2.5 along the Ikorodu road. Hence, the government’s “Non- Motorised Transport (NMT)” policy could enhance reduction of PM2.5 emission along the Ikorodu road