State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

No abstract availabl

Neuro-Fuzzy Motion Planning for Robotic Manipulators

On-going research efforts in robotics aim at providing mechanical systems, such as robotic manipulators and mobile robots, with more intelligence so that they can operate autonomously. Advancing in this direction, this thesis proposes and investigates novel manipulator path planning and navigation techniques which have their roots in the field of neural networks and fuzzy logic. Path planning in the configuration space makes necessary a transformation of the workspace into a configuration space. A radial-basis-function neural network is proposed to construct the configuration space by repeatedly mapping individual workspace obstacle points into so-called C-space patterns. The method is extended to compute the transformation for planar manipulators with n links as well as for manipulators with revolute and prismatic joints. A neural-network-based implementation of a computer emulated resistive grid is described and investigated. The grid, which is a collection of nodes laterally connected by weights, carries out global path planning in the manipulator’s configuration space. In response to a specific obstacle constellation, the grid generates an activity distribution whose gradient can be exploited to construct collision-free paths. A novel update algorithm, the To&Fro algorithm, which rapidly spreads the activity distribution over the nodes is proposed. Extensions to the basic grid technique are presented. A novel fuzzy-based system, the fuzzy navigator, is proposed to solve the navigation and obstacle avoidance problem for robotic manipulators. The presented system is divided into separate fuzzy units which individually control each manipulator link. The competing functions of goal following and obstacle avoidance are combined in each unit providing an intelligent behaviour. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment. All above methods have been tested in different environments on simulated manipulators as well as on a physical manipulator. The results proved these methods to be feasible for real-world applications

AI gym for Networks

5G Networks are delivering better services and connecting more devices, but at the same time are becoming more complex. Problems like resource management and control optimization are increasingly dynamic and difficult to model making it very hard to use traditional model-based optimization techniques. Artificial Intelligence (AI) explores techniques such as Deep Reinforcement Learning (DRL), which uses the interaction between the agent and the environment to learn what action to take to obtain the best possible result. Researchers usually need to create and develop a simulation environment for their scenario of interest to be able to experiment with DRL algorithms. This takes a large amount of time from the research process, while the lack of a common environment makes it difficult to compare algorithms. The proposed solution aims to fill this gap by creating a tool that facilitates the setting up of DRL training environments for network scenarios. The developed tool uses three open source software, the Containernet to simulate the connections between devices, the Ryu Controller as the Software Defined Network Controller, and OpenAI Gym which is responsible for setting up the communication between the environment and the DRL agent. With the project developed during the thesis, the users will be capable of creating more scenarios in a short period, opening space to set up different environments, solving various problems as well as providing a common environment where other Agents can be compared. The developed software is used to compare the performance of several DRL agents in two different network control problems: routing and network slice admission control. A novel DRL based solution is used in the case of network slice admission control that jointly optimizes the admission and the placement of traffic of a network slice in the physical resources.As redes 5G oferecem melhores serviços e conectam mais dispositivos, fazendo com que se tornem mais complexas e difíceis de gerir. Problemas como a gestão de recursos e a otimização de controlo são cada vez mais dinâmicos e difíceis de modelar, o que torna difícil usar soluções de optimização basea- das em modelos tradicionais. A Inteligência Artificial (IA) explora técnicas como Deep Reinforcement Learning que utiliza a interação entre o agente e o ambiente para aprender qual a ação a ter para obter o melhor resultado possível. Normalmente, os investigadores precisam de criar e desenvolver um ambiente de simulação para poder estudar os algoritmos DRL e a sua interação com o cenário de interesse. A criação de ambientes a partir do zero retira tempo indispensável para a pesquisa em si, e a falta de ambientes de treino comuns torna difícil a comparação dos algoritmos. A solução proposta foca-se em preencher esta lacuna criando uma ferramenta que facilite a configuração de ambientes de treino DRL para cenários de rede. A ferramenta desenvolvida utiliza três softwares open source, o Containernet para simular as conexões entre os dispositivos, o Ryu Controller como Software Defined Network Controller e o OpenAI Gym que é responsável por configurar a comunicação entre o ambiente e o agente DRL. Através do projeto desenvolvido, os utilizadores serão capazes de criar mais cenários em um curto período, abrindo espaço para configurar diferentes ambientes e resolver diferentes problemas, bem como fornecer um ambiente comum onde diferentes Agentes podem ser comparados. O software desenvolvido foi usado para comparar o desempenho de vários agentes DRL em dois problemas diferentes de controlo de rede, nomeadamente, roteamento e controlo de admissão de slices na rede. Uma solução baseada em DRL é usada no caso do controlo de admissão de slices na rede que otimiza conjuntamente a admissão e a colocação de tráfego de uma slice na rede nos recursos físicos da mesma

System diagnosis using a bayesian method

Today’s engineering systems have become increasingly more complex. This makes fault diagnosis a more challenging task in industry and therefore a significant amount of research has been undertaken on developing fault diagnostic methodologies. So far there already exist a variety of diagnostic methods, from qualitative to quantitative. However, no methods have considered multi-component degradation when diagnosing faults at the system level. For example, from the point a new aircraft takes off for the first time all of its components start to degrade, and yet in previous studies it is presumed that apart from the faulty component, other components in the system are operating in a healthy state. This thesis makes a contribution through the development of an experimental fuel rig to produce high quality data of multi-component degradation and a probabilistic framework based on the Bayesian method to diagnose faults in a system with considering multi-component degradation. The proposed method is implemented on the fuel rig data which illustrates the applicability of the proposed method and the diagnostic results are compared with the neural network method in order to show the capabilities and imperfections of the proposed method