An Intelligent System for Mining and Maintaining Correlation Patterns among Appliances in Smart Home

[[abstract]]Recently, due to the great advent of sensor technology, residents can collect the usage data of appliances in a house easily. However, with data progressively generating, it is still a challenge to visualize how these appliances are used. Thus, a mining and maintaining system is needed to incrementally discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance and do not consider the incremental maintenance of mining results. In this paper, a novel system, namely, Dynamic Correlation Mining System (DCMS) is developed to capture and maintain the correlation patterns among appliances incrementally. The experimental results indicate that proposed system is efficient in execution time and possesses scalability. Furthermore, we apply DCMS on a real-world dataset to show the practicability.[[conferencetype]]國內[[conferencedate]]20140826~20140827[[booktype]]紙本[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tainan, Taiwa

Revisiting the Technology Challenges and Proposing Enhancements in Ambient Assisted Living for the Elderly

Several social and technical trends support the elderly’s desire to live independently in their preferred environment, despite their increasing medical needs, and enhance their quality of life at home. Ambient-assisted living (AAL) has the capabilities to support the elderly and to decrease their dependency on formal or informal caregivers. We provide a review of the technological challenges that were identified as inhibiting factors in the past decade and then present recent technological advances, e.g., cloud computing, machine learning, artificial intelligence, the Internet of Things. We also fill the gap in the current literature in regard to specific AAL solutions and propose fourth-generation AAL technology design. We find that most informal caregivers are family members who are medically untrained and that the use of advanced analytical processes on AAL-generated data could significantly increase symptom identification. We also present the implications and remaining challenges along with recommendations for future research

A multidisciplinary research approach to energy-related behavior in buildings

Occupant behavior in buildings is one of the key drivers of building energy performance. Closing the “performance gap” in the building sector requires a deeper understanding and consideration of the “human factor” in energy usage. For Europe and US to meet their challenging 2020 and 2050 energy and GHG reduction goals, we need to harness the potential savings of human behavior in buildings, in addition to deployment of energy efficient technologies and energy policies for buildings. Through involvement in international projects such as IEA ECBC Annex 53 and EBC Annex 66, the research conducted in the context of this thesis provided significant contributions to understand occupants’ interactions with building systems and to reduce their energy use in residential and commercial buildings over the entire building life cycle. The primary goal of this Ph.D. study is to explore and highlight the human factor in energy use as a fundamental aspect influencing the energy performance of buildings and maximizing energy efficiency – to the same extent as technological innovation. Scientific literature was reviewed to understand state-of-the-art gaps and limitations of research in the field. Human energy-related behavior in buildings emerges a stochastic and highly complex problem, which cannot be solved by one discipline alone. Typically, a technological-social dichotomy pertains to the human factor in reducing energy use in buildings. Progressing past that, this research integrates occupant behavior in a multidisciplinary approach that combines insights from the technical, analytical and social dimension. This is achieved by combining building physics (occupant behavior simulation in building energy models to quantify impact on building performance) and data science (data mining, analytics, modeling and profiling of behavioral patterns in buildings) with behavioral theories (engaging occupants and motivating energy-saving occupant behaviors) to provide multidisciplinary, innovative insights on human-centered energy efficiency in buildings. The systematic interconnection of these three dimensions is adopted at different scales. The building system is observed at the residential and commercial level. Data is gathered, then analyzed, modeled, standardized and simulated from the zone to the building level, up to the district scale. Concerning occupant behavior, this research focuses on individual, group and collective actions. Various stakeholders can benefit from this Ph.D. dissertation results. Audience of the research includes energy modelers, architects, HVAC engineers, operators, owners, policymakers, building technology vendors, as well as simulation program designers, implementers and evaluators. The connection between these different levels, research foci and targeted audience is not linear among the three observed systems. Rather, the multidisciplinary research approach to energy-related behavior in buildings proposed by this Ph.D. study has been adopted to explore solutions that could overcome the limitations and shortcomings in the state-of-the-art research

Context-induced activity monitoring for on-demand things-of-interest recommendation in an ambient intelligent environment

Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications

Architecture and Applications of IoT Devices in Socially Relevant Fields

Number of IoT enabled devices are being tried and introduced every year and there is a healthy competition among researched and businesses to capitalize the space created by IoT, as these devices have a great market potential. Depending on the type of task involved and sensitive nature of data that the device handles, various IoT architectures, communication protocols and components are chosen and their performance is evaluated. This paper reviews such IoT enabled devices based on their architecture, communication protocols and functions in few key socially relevant fields like health care, farming, firefighting, women/individual safety/call for help/harm alert, home surveillance and mapping as these fields involve majority of the general public. It can be seen, to one's amazement, that already significant number of devices are being reported on these fields and their performance is promising. This paper also outlines the challenges involved in each of these fields that require solutions to make these devices reliableComment: 1

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

Interacting with Smart Environments: Users, Interfaces, and Devices

A Smart Environment is an environment enriched with disappearing devices, acting together to form an “intelligent entity”. In such environments, the computing power pervades the space where the user lives, so it becomes particularly important to investigate the user’s perspective in interacting with her surrounding. Interaction, in fact, occurs when a human performs some kind of activity using any computing technology: in this case, the computing technology has an intelligence of its own and can potentially be everywhere. There is no well-defined interaction situation or context, and interaction can happen casually or accidentally. The objective of this dissertation is to improve the interaction between such complex and different entities: the human and the Smart Environment. To reach this goal, this thesis presents four different and innovative approaches to address some of the identified key challenges. Such approaches, then, are validated with four corresponding software solutions, integrated with a Smart Environment, that I have developed and tested with end-users. Taken together, the proposed solutions enable a better interaction between diverse users and their intelligent environments, provide a solid set of requirements, and can serve as a baseline for further investigation on this emerging topic

Knowledge Distillation and Continual Learning for Optimized Deep Neural Networks

Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations. Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally learning new tasks, the model performance on old tasks significantly drops. The ability to accommodate new knowledge while retaining previously learned knowledge is called continual learning. Since the realworld environments in which the model operates are always evolving, a robust neural network needs to have this continual learning ability for adapting to new changes

Hardware and software platforms to deploy and evaluate non-intrusive load monitoring systems

The work in this PhD thesis addresses the practical implications of deploying and testing Non-Intrusive Load Monitoring (NILM) and eco-feedback solutions in real-world scenarios. The contributions to this topic are centered around the design and development of NILM frameworks that have been deployed in the wild, supporting long-term research in ecofeedback and also serving the purpose of producing real-world datasets and furthering the state of the art regarding the performance metrics used to evaluate NILM algorithms. This thesis consists of three main parts: i) the development of tools and datasets for NILM and eco-feedback research, ii) the design, implementation and deployment of NILM and eco-feedback technologies in real world scenarios, and iii) an experimental comparison of performance metrics for event detection and event classification algorithms. In the first part we describe the Energy Monitoring and Disaggregation Data Format (EMD-DF) and the SustData and SustDataED public datasets. In second part we discuss the development and deployment of two hardware and software platforms in real households, to support eco-feedback research. We then report on more than five years of experience in deploying and maintaining such platforms. Our findings suggest that the main practical issues can be divided in two categories, technological (e.g., system installation) and social (e.g., maintaining a steady sample throughout the whole study). In the final part of this thesis we analyze experimentally the behavior of a number of performance metrics for event detection and event classification, identifying clusters and relationships between the different measures. Our results evidence some considerable differences in the behavior of the performance metrics when applied to the different problems.O trabalho desenvolvido nesta tese de doutoramento aborda as implicações praticas da instalação e avaliação de soluções de monitorização não intrusiva de cargas elétricas (NILM) e eco-feedback em cenários reais. As contribuições para este tópico estão centradas em torno da concepção e desenvolvimento de plataformas NILM que foram instaladas em ambientes não controlados, suportando a pesquisa de longo termo em eco-feedback e servindo também o propósito de produzir conjuntos de dados científicos, bem como promover o avanço do estado da arte acerca das métricas de desempenho utilizadas para avaliar algoritmos NILM. Esta tese é constituída por três partes principais: i) o desenvolvimento de ferramentas e conjuntos de dados científicos para investigação em NILM e eco-feedback, ii) a concepção, desenho e instalação de tecnologias NILM e eco-feedback em cenários reais, e iii) uma comparação experimental de métricas de desempenho para algoritmos de detecção e de classificação de eventos. Na primeira parte descrevemos o Energy Monitoring and Disaggregation Data Format (EMD-DF) e os conjuntos de dados científicos SustData e SustDataED. Na segunda parte discutimos o desenvolvimento e instalação de duas plataformas de hardware e software em residências atuais com a finalidade de suportar a investigação em eco-feedback. Aqui, reportamos sobre mais de cinco anos de experiência na instalação e manutenção destes sistemas. Os nossos resultados sugerem que as principais implicações práticas podem ser divididas em duas categorias, físicas (e.g., instalação do sistema) e sociais (e.g., manter uma amostra constante ao longo de todo o estudo). Na terceira parte analisamos experimentalmente o comportamento de uma série de métricas de desempenho quando estas são utilizadas para avaliar algoritmos de detecção e de classificação de eventos. Calculamos as correlações lineares e não lineares entre os vários pares de métricas, e com base nesses valores procuramos agrupar as métricas que evidenciam um comportamento semelhante. Os nossos resultados sugerem a existência de diferenças evidentes no comportamento das métricas quando aplicadas a ambos dos problemas.Fundação para a Ciência e a Tecnologi