13,582 research outputs found
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway
The development of Smart Grid in Norway in specific and Europe/US in general
will shortly lead to the availability of massive amount of fine-grained
spatio-temporal consumption data from domestic households. This enables the
application of data mining techniques for traditional problems in power system.
Clustering customers into appropriate groups is extremely useful for operators
or retailers to address each group differently through dedicated tariffs or
customer-tailored services. Currently, the task is done based on demographic
data collected through questionnaire, which is error-prone. In this paper, we
used three different clustering techniques (together with their variants) to
automatically segment electricity consumers based on their consumption
patterns. We also proposed a good way to extract consumption patterns for each
consumer. The grouping results were assessed using four common internal
validity indexes. We found that the combination of Self Organizing Map (SOM)
and k-means algorithms produce the most insightful and useful grouping. We also
discovered that grouping quality cannot be measured effectively by automatic
indicators, which goes against common suggestions in literature.Comment: 12 pages, 3 figure
Towards a Practical Pedestrian Distraction Detection Framework using Wearables
Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices
A Machine Learning-Based Framework for Clustering Residential Electricity Load Profiles to Enhance Demand Response Programs
Load shapes derived from smart meter data are frequently employed to analyze
daily energy consumption patterns, particularly in the context of applications
like Demand Response (DR). Nevertheless, one of the most important challenges
to this endeavor lies in identifying the most suitable consumer clusters with
similar consumption behaviors. In this paper, we present a novel machine
learning based framework in order to achieve optimal load profiling through a
real case study, utilizing data from almost 5000 households in London. Four
widely used clustering algorithms are applied specifically K-means, K-medoids,
Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An
empirical analysis as well as multiple evaluation metrics are leveraged to
assess those algorithms. Following that, we redefine the problem as a
probabilistic classification one, with the classifier emulating the behavior of
a clustering algorithm,leveraging Explainable AI (xAI) to enhance the
interpretability of our solution. According to the clustering algorithm
analysis the optimal number of clusters for this case is seven. Despite that,
our methodology shows that two of the clusters, almost 10\% of the dataset,
exhibit significant internal dissimilarity and thus it splits them even further
to create nine clusters in total. The scalability and versatility of our
solution makes it an ideal choice for power utility companies aiming to segment
their users for creating more targeted Demand Response programs.Comment: 29 pages, 19 figure
Automated Home Oxygen Delivery for Patients with COPD and Respiratory Failure: A New Approach
Long-term oxygen therapy (LTOT) has become standard care for the treatment of patients with chronic obstructive pulmonary disease (COPD) and other severe hypoxemic lung diseases. The use of new portable O-2 concentrators (POC) in LTOT is being expanded. However, the issue of oxygen titration is not always properly addressed, since POCs rely on proper use by patients. The robustness of algorithms and the limited reliability of current oximetry sensors are hindering the effectiveness of new approaches to closed-loop POCs based on the feedback of blood oxygen saturation. In this study, a novel intelligent portable oxygen concentrator (iPOC) is described. The presented iPOC is capable of adjusting the O-2 flow automatically by real-time classifying the intensity of a patient's physical activity (PA). It was designed with a group of patients with COPD and stable chronic respiratory failure. The technical pilot test showed a weighted accuracy of 91.1% in updating the O-2 flow automatically according to medical prescriptions, and a general improvement in oxygenation compared to conventional POCs. In addition, the usability achieved was high, which indicated a significant degree of user satisfaction. This iPOC may have important benefits, including improved oxygenation, increased compliance with therapy recommendations, and the promotion of PA
An Unsupervised Cluster: Learning Water Customer Behavior Using Variation of Information on a Reconstructed Phase Space
The unsupervised clustering algorithm described in this dissertation addresses the need to divide a population of water utility customers into groups based on their similarities and differences, using only the measured flow data collected by water meters. After clustering, the groups represent customers with similar consumption behavior patterns and provide insight into ‘normal’ and ‘unusual’ customer behavior patterns. This research focuses upon individually metered water utility customers and includes both residential and commercial customer accounts serviced by utilities within North America. The contributions of this dissertation not only represent a novel academic work, but also solve a practical problem for the utility industry. This dissertation introduces a method of agglomerative clustering using information theoretic distance measures on Gaussian mixture models within a reconstructed phase space. The clustering method accommodates a utility’s limited human, financial, computational, and environmental resources. The proposed weighted variation of information distance measure for comparing Gaussian mixture models places emphasis upon those behaviors whose statistical distributions are more compact over those behaviors with large variation and contributes a novel addition to existing comparison options
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