Energy-based decision engine for household human activity recognition

We propose a framework for energy-based human activity recognition in a household environment. We apply machine learning techniques to infer the state of household appliances from their energy consumption data and use rulebased scenarios that exploit these states to detect human activity. Our decision engine achieved a 99.1% accuracy for real-world data collected in the kitchens of two smart homes

Perspectives on subnational carbon and climate footprints: A case study of Southampton, UK

Sub-national governments are increasingly interested in local-level climate change management. Carbon- (CO2 and CH4) and climate-footprints—(Kyoto Basket GHGs) (effectively single impact category LCA metrics, for global warming potential) provide an opportunity to develop models to facilitate effective mitigation. Three approaches are available for the footprinting of sub-national communities. Territorial-based approaches, which focus on production emissions within the geo-political boundaries, are useful for highlighting local emission sources but do not reflect the transboundary nature of sub-national community infrastructures. Transboundary approaches, which extend territorial footprints through the inclusion of key cross boundary flows of materials and energy, are more representative of community structures and processes but there are concerns regarding comparability between studies. The third option, consumption-based, considers global GHG emissions that result from final consumption (households, governments, and investment). Using a case study of Southampton, UK, this chapter develops the data and methods required for a sub-national territorial, transboundary, and consumption-based carbon and climate footprints. The results and implication of each footprinting perspective are discussed in the context of emerging international standards. The study clearly shows that the carbon footprint (CO2 and CH4 only) offers a low-cost, low-data, universal metric of anthropogenic GHG emission and subsequent management

Machine Learning for Human Activity Detection in Smart Homes

Recognizing human activities in domestic environments from audio and active power consumption sensors is a challenging task since on the one hand, environmental sound signals are multi-source, heterogeneous, and varying in time and on the other hand, the active power consumption varies significantly for similar type electrical appliances. Many systems have been proposed to process environmental sound signals for event detection in ambient assisted living applications. Typically, these systems use feature extraction, selection, and classification. However, despite major advances, several important questions remain unanswered, especially in real-world settings. A part of this thesis contributes to the body of knowledge in the field by addressing the following problems for ambient sounds recorded in various real-world kitchen environments: 1) which features, and which classifiers are most suitable in the presence of background noise? 2) what is the effect of signal duration on recognition accuracy? 3) how do the SNR and the distance between the microphone and the audio source affect the recognition accuracy in an environment in which the system was not trained? We show that for systems that use traditional classifiers, it is beneficial to combine gammatone frequency cepstral coefficients and discrete wavelet transform coefficients and to use a gradient boosting classifier. For systems based on deep learning, we consider 1D and 2D CNN using mel-spectrogram energies and mel-spectrograms images, as inputs, respectively and show that the 2D CNN outperforms the 1D CNN. We obtained competitive classification results for two such systems and validated the performance of our algorithms on public datasets (Google Brain/TensorFlow Speech Recognition Challenge and the 2017 Detection and Classification of Acoustic Scenes and Events Challenge). Regarding the problem of the energy-based human activity recognition in a household environment, machine learning techniques to infer the state of household appliances from their energy consumption data are applied and rule-based scenarios that exploit these states to detect human activity are used. Since most activities within a house are related with the operation of an electrical appliance, this unimodal approach has a significant advantage using inexpensive smart plugs and smart meters for each appliance. This part of the thesis proposes the use of unobtrusive and easy-install tools (smart plugs) for data collection and a decision engine that combines energy signal classification using dominant classifiers (compared in advanced with grid search) and a probabilistic measure for appliance usage. It helps preserving the privacy of the resident, since all the activities are stored in a local database. DNNs received great research interest in the field of computer vision. In this thesis we adapted different architectures for the problem of human activity recognition. We analyze the quality of the extracted features, and more specifically how model architectures and parameters affect the ability of the automatically extracted features from DNNs to separate activity classes in the final feature space. Additionally, the architectures that we applied for our main problem were also applied to text classification in which we consider the input text as an image and apply 2D CNNs to learn the local and global semantics of the sentences from the variations of the visual patterns of words. This work helps as a first step of creating a dialogue agent that would not require any natural language preprocessing. Finally, since in many domestic environments human speech is present with other environmental sounds, we developed a Convolutional Recurrent Neural Network, to separate the sound sources and applied novel post-processing filters, in order to have an end-to-end noise robust system. Our algorithm ranked first in the Apollo-11 Fearless Steps Challenge.Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 676157, project ACROSSIN

Intelligent Coordination and Automation for Smart Home Accessories

Smarthome accessories are rapidly becoming more popular. Although many companies are making devices to take advantage of this market, most of the created smart devices are actually unintelligent. Currently, these smart home devices require meticulous, tedious configuration to get any sort of enhanced usability over their analog counterparts. We propose building a general model using machine learning and data science to automatically learn a user\u27s smart accessory usage to predict their configuration. We have identified the requirements, collected data, recognized the risks, implemented the system, and have met the goals we set out to accomplish

Ecodriving and Carbon Footprinting: Understanding How Public Education Can Reduce Greenhouse Gas Emissions and Fuel Use

Ecodriving is a collection of changes to driving behavior and vehicle maintenance designed to impact fuel consumption and greenhouse gas (GHG) emissions in existing vehicles. Because of its promise to improve fuel economy within the existing fleet, ecodriving has gained increased attention in North America. One strategy to improve ecodriving is through public education with information on how to ecodrive. This report provides a review and study of ecodriving from several angles. The report offers a literature review of previous work and programs in ecodriving across the world. In addition, researchers completed interviews with experts in the field of public relations and public message campaigns to ascertain best practices for public campaigns. Further, the study also completed a set of focus groups evaluating consumer response to a series of websites that displayed ecodriving information. Finally, researchers conducted a set of surveys, including a controlled stated-response study conducted with approximately 100 University of California, Berkeley faculty, staff, and students, assessing the effectiveness of static ecodriving web-based information as well as an intercept clipboard survey in the San Francisco Bay Area. The stated-response study consisted of a comparison of the experimental and control groups. It found that exposure to ecodriving information influenced people’s driving behavior and some maintenance practices. The experimental group’s distributional shift was statistically significant, particularly for key practices including: lower highway cruising speed, driving behavior adjustment, and proper tire inflation. Within the experimental group (N = 51), fewer respondents significantly changed their maintenance practices (16%) than the majority that altered some driving practices (71%). This suggests intentionally altering driving behavior is easier than planning better maintenance practices. While it was evident that not everyone modifies their behavior as a result of reviewing the ecodriving website, even small shifts in behavior due to inexpensive information dissemination could be deemed cost effective in reducing fuel consumption and emissions

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for solving time series data mining problems. However, to the best of our knowledge, no previous work has considered the vulnerability of deep learning models to adversarial time series examples, which could potentially make them unreliable in situations where the decision taken by the classifier is crucial such as in medicine and security. For computer vision problems, such attacks have been shown to be very easy to perform by altering the image and adding an imperceptible amount of noise to trick the network into wrongly classifying the input image. Following this line of work, we propose to leverage existing adversarial attack mechanisms to add a special noise to the input time series in order to decrease the network's confidence when classifying instances at test time. Our results reveal that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks which can have major consequences in multiple domains such as food safety and quality assurance.Comment: Accepted at IJCNN 201

Mitigation factors

The debate over the costs of GHG emission reduction has become more com-plex recently as disagreements over the existence of economic and environ-mental double dividents have been added to discussions over the existence of a negative cost potential. We argue that basic assumptions about economic effi-ciency, the (sub-)optimality of the baseline and the rate of technical change are more important than model structure, and we underline the importance of the timing of decisions for determining the costs. Moreover the use of a single baseline ‘no policy' scenario and several policy intervention scenarios may be fundamentally misleading in the longer term simply because the very idea of a business as usual scenario is deeply problematic. Ultimately the debate turns on political judgments about the desirability of alternative development paths. Copright© 1996 Elsevier Science Ltd.Greenhouse gas emissions;Costs of GHG reduction; Mitigation options