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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
The detection performance of small objects in remote sensing images is not
satisfactory compared to large objects, especially in low-resolution and noisy
images. A generative adversarial network (GAN)-based model called enhanced
super-resolution GAN (ESRGAN) shows remarkable image enhancement performance,
but reconstructed images miss high-frequency edge information. Therefore,
object detection performance degrades for small objects on recovered noisy and
low-resolution remote sensing images. Inspired by the success of edge enhanced
GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN
(EESRGAN) to improve the image quality of remote sensing images and use
different detector networks in an end-to-end manner where detector loss is
backpropagated into the EESRGAN to improve the detection performance. We
propose an architecture with three components: ESRGAN, Edge Enhancement Network
(EEN), and Detection network. We use residual-in-residual dense blocks (RRDB)
for both the ESRGAN and EEN, and for the detector network, we use the faster
region-based convolutional network (FRCNN) (two-stage detector) and single-shot
multi-box detector (SSD) (one stage detector). Extensive experiments on a
public (car overhead with context) and a self-assembled (oil and gas storage
tank) satellite dataset show superior performance of our method compared to the
standalone state-of-the-art object detectors.Comment: This paper contains 27 pages and accepted for publication in MDPI
remote sensing journal. GitHub Repository:
https://github.com/Jakaria08/EESRGAN (Implementation
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks
Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load
disaggregation algorithms, are fundamental tools for effective energy
management. Despite the success of deep models in load disaggregation, they
face various challenges, particularly those pertaining to privacy and security.
This paper investigates the sensitivity of prominent deep NILM baselines to
adversarial attacks, which have proven to be a significant threat in domains
such as computer vision and speech recognition. Adversarial attacks entail the
introduction of imperceptible noise into the input data with the aim of
misleading the neural network into generating erroneous outputs. We investigate
the Fast Gradient Sign Method (FGSM), a well-known adversarial attack, to
perturb the input sequences fed into two commonly employed CNN-based NILM
baselines: the Sequence-to-Sequence (S2S) and Sequence-to-Point (S2P) models.
Our findings provide compelling evidence for the vulnerability of these models,
particularly the S2P model which exhibits an average decline of 20\% in the
F1-score even with small amounts of noise. Such weakness has the potential to
generate profound implications for energy management systems in residential and
industrial sectors reliant on NILM models
Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities
The built environment is responsible for roughly 40% of global greenhouse emissions, making the sector a crucial factor for climate change and sustainability. Meanwhile, other sectors (like manufacturing) adopted Artificial Intelligence (AI) to solve complex, non-linear problems to reduce waste, inefficiency, and pollution. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management (AM) processes. Since AM encompasses a broad set of disciplines, an overview of several AI applications, current research gaps, and trends is needed. In this context, this study conducted the first state-of-the-art research on AI for building asset management. A total of 578 papers were analyzed with bibliometric tools to identify prominent institutions, topics, and journals. The quantitative analysis helped determine the most researched areas of AM and which AI techniques are applied. The areas were furtherly investigated by reading in-depth the 83 most relevant studies selected by screening the articles’ abstracts identified in the bibliometric analysis. The results reveal many applications for Energy Management, Condition assessment, Risk management, and Project management areas. Finally, the literature review identified three main trends that can be a reference point for future studies made by practitioners or researchers: Digital Twin, Generative Adversarial Networks (with synthetic images) for data augmentation, and Deep Reinforcement Learning
Context-Aware Design of Cyber-Physical Human Systems (CPHS)
Recently, it has been widely accepted by the research community that
interactions between humans and cyber-physical infrastructures have played a
significant role in determining the performance of the latter. The existing
paradigm for designing cyber-physical systems for optimal performance focuses
on developing models based on historical data. The impacts of context factors
driving human system interaction are challenging and are difficult to capture
and replicate in existing design models. As a result, many existing models do
not or only partially address those context factors of a new design owing to
the lack of capabilities to capture the context factors. This limitation in
many existing models often causes performance gaps between predicted and
measured results. We envision a new design environment, a cyber-physical human
system (CPHS) where decision-making processes for physical infrastructures
under design are intelligently connected to distributed resources over
cyberinfrastructure such as experiments on design features and empirical
evidence from operations of existing instances. The framework combines existing
design models with context-aware design-specific data involving
human-infrastructure interactions in new designs, using a machine learning
approach to create augmented design models with improved predictive powers.Comment: Paper was accepted at the 12th International Conference on
Communication Systems and Networks (COMSNETS 2020
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