519 research outputs found
Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data
Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%
A Review On Automatic Text Summarization Approaches
It has been more than 50 years since the initial investigation on automatic text summarization was started.Various techniques have been successfully used to extract the important contents from text document to represent document summary.In this study,we review some of the studies that have been conducted in this still-developing research area.It covers the basics of text summarization,the types of summarization,the methods that
have been used and some areas in which text summarization has been applied.Furthermore,this paper also reviews the significant efforts which have been put in studies concerning sentence extraction,domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it.In addition,the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames
We propose scene summarization as a new video-based scene understanding task.
It aims to summarize a long video walkthrough of a scene into a small set of
frames that are spatially diverse in the scene, which has many impotant
applications, such as in surveillance, real estate, and robotics. It stems from
video summarization but focuses on long and continuous videos from moving
cameras, instead of user-edited fragmented video clips that are more commonly
studied in existing video summarization works. Our solution to this task is a
two-stage self-supervised pipeline named SceneSum. Its first stage uses
clustering to segment the video sequence. Our key idea is to combine visual
place recognition (VPR) into this clustering process to promote spatial
diversity. Its second stage needs to select a representative keyframe from each
cluster as the summary while respecting resource constraints such as memory and
disk space limits. Additionally, if the ground truth image trajectory is
available, our method can be easily augmented with a supervised loss to enhance
the clustering and keyframe selection. Extensive experiments on both real-world
and simulated datasets show our method outperforms common video summarization
baselines by 50
Fault Detection and Diagnosis Encyclopedia for Building Systems:A Systematic Review
This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository
Analysis on the Application of Machine-Learning Algorithms for District-Heating Networks' Characterization & Management
359 p.Esta tesis doctoral estudia la viabilidad de la aplicación de algoritmos de aprendizaje automático para la caracterización energética de los edificios en entornos de redes de calefacción urbana. En particular, la disertación se centrará en el análisis de las siguientes cuatro aplicaciones principales: (i)La identificación y eliminación de valores atÃpicos de demanda en los edificios; (ii) Reconocimiento de los principales patrones de demanda energética en edificios conectados a la red. (iii) Estudio de interpretabilidad/clasificación de dichos patrones energéticos. Análisis descriptivo de los patrones de la demanda. (iv) Predicción de la demanda de energÃa en resolución diaria y horaria.El interés de la tesis fue despertado por la situación energética actual en la Unión Europea, donde los edificios son responsables de más del 40% del consumo total de energÃa. Las redes de distrito modernas han sido identificadas como sistemas eficientes para el suministro de energÃa desde las plantas de producción hasta los consumidores finales/edificios debido a su economÃa de escala. Además, debido a la agrupación de edificios en una misma red, permitirán el desarrollo e implementación de algoritmos para la gestión de la energÃa en el sistema completo
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Identification and prediction of abnormal behaviour activities of daily living in intelligent environments
The aim of this research is to investigate efficient mining of useful information from a sensor network forming an Ambient Intelligence (AmI) environment. In this thesis, we investigate methods for supporting independent living of the elderly (and specifically patients who are suffering from dementia) by means of equipping their home with a simple sensor network to monitor their behaviour and identify their Activities of Daily Living (ADL). Dementia is considered to be one of the most important causes of disability in the elderly. Mostpatients would prefer to use non-intrusive technology to help them tomaintain their independence. Such monitoring and prediction would allow the caregiver to see any trend in the behaviour of the elderly person and to be informed of any abnormal behaviour
WEATHER LORE VALIDATION TOOL USING FUZZY COGNITIVE MAPS BASED ON COMPUTER VISION
Published ThesisThe creation of scientific weather forecasts is troubled by many technological challenges (Stern
& Easterling, 1999) while their utilization is generally dismal. Consequently, the majority of
small-scale farmers in Africa continue to consult some forms of weather lore to reach various
cropping decisions (Baliscan, 2001). Weather lore is a body of informal folklore (Enock, 2013),
associated with the prediction of the weather, and based on indigenous knowledge and human
observation of the environment. As such, it tends to be more holistic, and more localized to the
farmers’ context. However, weather lore has limitations; for instance, it has an inability to offer
forecasts beyond a season. Different types of weather lore exist, utilizing almost all available
human senses (feel, smell, sight and hearing). Out of all the types of weather lore in existence, it
is the visual or observed weather lore that is mostly used by indigenous societies, to come up
with weather predictions.
On the other hand, meteorologists continue to treat this knowledge as superstition, partly because
there is no means to scientifically evaluate and validate it. The visualization and characterization
of visual sky objects (such as moon, clouds, stars, and rainbows) in forecasting weather are
significant subjects of research. To realize the integration of visual weather lore in modern
weather forecasting systems, there is a need to represent and scientifically substantiate this form
of knowledge. This research was aimed at developing a method for verifying visual weather lore that is used by
traditional communities to predict weather conditions. To realize this verification, fuzzy
cognitive mapping was used to model and represent causal relationships between selected visual
weather lore concepts and weather conditions. The traditional knowledge used to produce these
maps was attained through case studies of two communities (in Kenya and South Africa).These
case studies were aimed at understanding the weather lore domain as well as the causal effects
between metrological and visual weather lore. In this study, common astronomical weather lore
factors related to cloud physics were identified as: bright stars, dispersed clouds, dry weather,
dull stars, feathery clouds, gathering clouds, grey clouds, high clouds, layered clouds, low
clouds, stars, medium clouds, and rounded clouds. Relationships between the concepts were also
identified and formally represented using fuzzy cognitive maps. On implementing the verification tool, machine vision was used to recognize sky objects
captured using a sky camera, while pattern recognition was employed in benchmarking and
scoring the objects. A wireless weather station was used to capture real-time weather parameters.
The visualization tool was then designed and realized in a form of software artefact, which
integrated both computer vision and fuzzy cognitive mapping for experimenting visual weather
lore, and verification using various statistical forecast skills and metrics. The tool consists of four
main sub-components: (1) Machine vision that recognizes sky objects using support vector
machine classifiers using shape-based feature descriptors; (2) Pattern recognition–to benchmark
and score objects using pixel orientations, Euclidean distance, canny and grey-level concurrence
matrix; (3) Fuzzy cognitive mapping that was used to represent knowledge (i.e. active hebbian
learning algorithm was used to learn until convergence); and (4) A statistical computing
component was used for verifications and forecast skills including brier score and contingency
tables for deterministic forecasts.
Rigorous evaluation of the verification tool was carried out using independent (not used in the
training and testing phases) real-time images from Bloemfontein, South Africa, and Voi-Kenya.
The real-time images were captured using a sky camera with GPS location services. The results
of the implementation were tested for the selected weather conditions (for example, rain, heat, cold, and dry conditions), and found to be acceptable (the verified prediction accuracies were
over 80%). The recommendation in this study is to apply the implemented method for processing
tasks, towards verifying all other types of visual weather lore. In addition, the use of the method
developed also requires the implementation of modules for processing and verifying other types
of weather lore, such as sounds, and symbols of nature. Since time immemorial, from Australia to Asia, Africa to Latin America, local communities have
continued to rely on weather lore observations to predict seasonal weather as well as its effects
on their livelihoods (Alcock, 2014). This is mainly based on many years of personal experiences
in observing weather conditions. However, when it comes to predictions for longer lead-times
(i.e. over a season), weather lore is uncertain (Hornidge & Antweiler, 2012). This uncertainty has
partly contributed to the current status where meteorologists and other scientists continue to treat
weather lore as superstition (United-Nations, 2004), and not capable of predicting weather.
One of the problems in testing the confidence in weather lore in predicting weather is due to
wide varieties of weather lore that are found in the details of indigenous sayings, which are
tightly coupled to locality and pattern variations(Oviedo et al., 2008). This traditional knowledge
is entrenched within the day-to-day socio-economic activities of the communities using it and is
not globally available for comparison and validation (Huntington, Callaghan, Fox, & Krupnik,
2004). Further, this knowledge is based on local experience that lacks benchmarking techniques;
so that harmonizing and integrating it within the science-based weather forecasting systems is a
daunting task (Hornidge & Antweiler, 2012). It is partly for this reason that the question of
validation of weather lore has not yet been substantially investigated. Sufficient expanded
processes of gathering weather observations, combined with comparison and validation, can produce some useful information. Since forecasting weather accurately is a challenge even with
the latest supercomputers (BBC News Magazine, 2013), validated weather lore can be useful if it
is incorporated into modern weather prediction systems.
Validation of traditional knowledge is a necessary step in the management of building integrated
knowledge-based systems. Traditional knowledge incorporated into knowledge-based systems
has to be verified for enhancing systems’ reliability. Weather lore knowledge exists in different
forms as identified by traditional communities; hence it needs to be tied together for comparison
and validation. The development of a weather lore validation tool that can integrate a framework
for acquiring weather data and methods of representing the weather lore in verifiable forms can
be a significant step in the validation of weather lore against actual weather records using
conventional weather-observing instruments. The success of validating weather lore could
stimulate the opportunity for integrating acceptable weather lore with modern systems of weather prediction to improve actionable information for decision making that relies on seasonal weather
prediction.
In this study a hybrid method is developed that includes computer vision and fuzzy cognitive
mapping techniques for verifying visual weather lore. The verification tool was designed with
forecasting based on mimicking visual perception, and fuzzy thinking based on the cognitive
knowledge of humans. The method provides meaning to humanly perceivable sky objects so that
computers can understand, interpret, and approximate visual weather outcomes.
Questionnaires were administered in two case study locations (KwaZulu-Natal province in South
Africa, and Taita-Taveta County in Kenya), between the months of March and July 2015. The
two case studies were conducted by interviewing respondents on how visual astronomical and
meteorological weather concepts cause weather outcomes. The two case studies were used to
identify causal effects of visual astronomical and meteorological objects to weather conditions.
This was followed by finding variations and comparisons, between the visual weather lore
knowledge in the two case studies. The results from the two case studies were aggregated in
terms of seasonal knowledge. The causal links between visual weather concepts were
investigated using these two case studies; results were compared and aggregated to build up
common knowledge. The joint averages of the majority of responses from the case studies were determined for each set of interacting concepts.
The modelling of the weather lore verification tool consists of input, processing components and
output. The input data to the system are sky image scenes and actual weather observations from
wireless weather sensors. The image recognition component performs three sub-tasks, including:
detection of objects (concepts) from image scenes, extraction of detected objects, and
approximation of the presence of the concepts by comparing extracted objects to ideal objects.
The prediction process involves the use of approximated concepts generated in the recognition
component to simulate scenarios using the knowledge represented in the fuzzy cognitive maps.
The verification component evaluates the variation between the predictions and actual weather
observations to determine prediction errors and accuracy.
To evaluate the tool, daily system simulations were run to predict and record probabilities of
weather outcomes (i.e. rain, heat index/hotness, dry, cold index). Weather observations were
captured periodically using a wireless weather station. This process was repeated several times until there was sufficient data to use for the verification process. To match the range of the
predicted weather outcomes, the actual weather observations (measurement) were transformed
and normalized to a range [0, 1].In the verification process, comparisons were made between the
actual observations and weather outcome prediction values by computing residuals (error values)
from the observations. The error values and the squared error were used to compute the Mean
Squared Error (MSE), and the Root Mean Squared Error (RMSE), for each predicted weather
outcome.
Finally, the validity of the visual weather lore verification model was assessed using data from a
different geographical location. Actual data in the form of daily sky scenes and weather
parameters were acquired from Voi, Kenya, from December 2015 to January 2016.The results on
the use of hybrid techniques for verification of weather lore is expected to provide an incentive
in integrating indigenous knowledge on weather with modern numerical weather prediction
systems for accurate and downscaled weather forecasts
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