92 research outputs found
Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques
Energy forecasting has a vital role to play in smart grid (SG) systems
involving various applications such as demand-side management, load shedding,
and optimum dispatch. Managing efficient forecasting while ensuring the least
possible prediction error is one of the main challenges posed in the grid
today, considering the uncertainty and granularity in SG data. This paper
presents a comprehensive and application-oriented review of state-of-the-art
forecasting methods for SG systems along with recent developments in
probabilistic deep learning (PDL) considering different models and
architectures. Traditional point forecasting methods including statistical,
machine learning (ML), and deep learning (DL) are extensively investigated in
terms of their applicability to energy forecasting. In addition, the
significance of hybrid and data pre-processing techniques to support
forecasting performance is also studied. A comparative case study using the
Victorian electricity consumption and American electric power (AEP) datasets is
conducted to analyze the performance of point and probabilistic forecasting
methods. The analysis demonstrates higher accuracy of the long-short term
memory (LSTM) models with appropriate hyper-parameter tuning among point
forecasting methods especially when sample sizes are larger and involve
nonlinear patterns with long sequences. Furthermore, Bayesian bidirectional
LSTM (BLSTM) as a probabilistic method exhibit the highest accuracy in terms of
least pinball score and root mean square error (RMSE)
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
CyberâPhysicalâSocial Frameworks for Urban Big Data Systems: A Survey
The integration of thingsâ data on the Web and Web linking for thingsâ description and discovery is leading the way towards smart CyberâPhysical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting CyberâPhysicalâSocial System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building humanâmachine intelligence
Theory and Algorithms for Reliable Multimodal Data Analysis, Machine Learning, and Signal Processing
Modern engineering systems collect large volumes of data measurements across diverse sensing modalities. These measurements can naturally be arranged in higher-order arrays of scalars which are commonly referred to as tensors. Tucker decomposition (TD) is a standard method for tensor analysis with applications in diverse fields of science and engineering. Despite its success, TD exhibits severe sensitivity against outliers âi.e., heavily corrupted entries that appear sporadically in modern datasets. We study L1-norm TD (L1-TD), a reformulation of TD that promotes robustness. For 3-way tensors, we show, for the first time, that L1-TD admits an exact solution via combinatorial optimization and present algorithms for its solution. We propose two novel algorithmic frameworks for approximating the exact solution to L1-TD, for general N-way tensors. We propose a novel algorithm for dynamic L1-TD âi.e., efficient and joint analysis of streaming tensors. Principal-Component Analysis (PCA) (a special case of TD) is also outlier responsive. We consider Lp-quasinorm PCA (Lp-PCA) for
Graph Representation Learning with Motif Structures
Graphs are important data structures that can be found in a wide variety of real-world scenarios. It is well recognised that the primitive graph representation is sparse, high- dimensional and noisy. Therefore, it is challenging to analyse such primitive data for downstream graph-related tasks (e.g., community detection and node classification). Graph representation learning (GRL) aims to map graph data into a low-dimensional dense vector space in which the graph information is maximally preserved. It allows primitive graphs to be easily analysed in the new mapped vector space.
GRL methods typically focus on simple connectivity patterns that only explicitly model relations between two nodes. Motif structures that capture relations among three or more nodes have been recognised as functional units of graphs, and can gain new insights into the organisation of graphs. Therefore, in this thesis we propose new GRL methods modelling motif structures for different graph-related tasks and applications along three directions: (1) a method to learn a spectral embedding space with both edge-based and triangle-based structures for clustering nodes; (2) a graph transformer by unifying homophily and heterophily representation for role classification and motif structure completion; (3) a method to tackle noises in knowledge graph representations with motif structures for recommendations and knowledge graph completion. Experimental studies show that the proposed methods have outperformed related state-of-the-art methods for targeted tasks and applications
Pattern classification approaches for breast cancer identification via MRI: stateâofâtheâart and vision for the future
Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI)
of breast tissue are discussed. The algorithms are based on recent advances in multidimensional
signal processing and aim to advance current stateâofâtheâart computerâaided detection
and analysis of breast tumours when these are observed at various states of development. The topics
discussed include image feature extraction, information fusion using radiomics, multiâparametric
computerâaided classification and diagnosis using information fusion of tensorial datasets as well
as Clifford algebra based classification approaches and convolutional neural network deep learning
methodologies. The discussion also extends to semiâsupervised deep learning and selfâsupervised
strategies as well as generative adversarial networks and algorithms using generated
confrontational learning approaches. In order to address the problem of weakly labelled tumour
images, generative adversarial deep learning strategies are considered for the classification of
different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence
(AI) based framework for more robust image registration that can potentially advance the early
identification of heterogeneous tumour types, even when the associated imaged organs are
registered as separate entities embedded in more complex geometric spaces. Finally, the general
structure of a highâdimensional medical imaging analysis platform that is based on multiâtask
detection and learning is proposed as a way forward. The proposed algorithm makes use of novel
loss functions that form the building blocks for a generated confrontation learning methodology
that can be used for tensorial DCEâMRI. Since some of the approaches discussed are also based on
timeâlapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The
proposed framework can potentially reduce the costs associated with the interpretation of medical
images by providing automated, faster and more consistent diagnosis
- âŠ