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Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants' presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%. [Display omitted] • This paper addresses the gap between the physiology and the psychology thermal comfort approach, dominated by AI solutions. • The work shows a wider comfort zone which has been identified to become progressively narrower over the past several decades. • The field studies represent major UK-dwelling cases that weren't addressed in the previous Artificial Intelligence approach. • The occupant presence and scheduling can contribute to more than a 10% increase in comfort which impacts energy saving. • This work highlights the possibility of achieving indoor thermal comfort with less energy for more sustainable dwellings. [ABSTRACT FROM AUTHOR]

Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants' presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%. [Display omitted] • This paper addresses the gap between the physiology and the psychology thermal comfort approach, dominated by AI solutions. • The work shows a wider comfort zone which has been identified to become progressively narrower over the past several decades. • The field studies represent major UK-dwelling cases that weren't addressed in the previous Artificial Intelligence approach. • The occupant presence and scheduling can contribute to more than a 10% increase in comfort which impacts energy saving. • This work highlights the possibility of achieving indoor thermal comfort with less energy for more sustainable dwellings. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive control system for coordinated metro operations with flexible train composition by using a multi-agent deep reinforcement learning (MADRL) approach. The control problem is formulated as a Markov decision process (MDP) with multiple agents regulating different service lines in a metro network with passenger transfer. To ensure the overall computational effectiveness and stability of the control system, we adopt an actor–critic reinforcement learning framework in which each control agent is associated with a critic function for estimating future system states and an actor function deriving local operational decisions. The critics and actors in the MADRL are represented by multi-layer artificial neural networks (ANNs). A multi-agent deep deterministic policy gradient (MADDPG) algorithm is developed for training the actor and critic ANNs through successive simulated transitions over the entire metro network. The developed framework is tested with a real-world scenario in Bakerloo and Victoria Lines of London Underground, UK. Experiment results demonstrate that the proposed method can outperform previous centralized optimization and distributed control approaches in terms of solution quality and performance achieved. Further analysis shows the merits of MADRL for coordinated service regulation with flexible train composition. This study contributes to real-time coordinated metro network services with flexible train composition and advanced optimization techniques. • An adaptive rail transit control system with passengers' transfers and flexible train composition. • A novel modeling and optimization framework based on multi-agent deep reinforcement learning. • A computational framework with 'decentralized execution and centralized training' for effectiveness and stability. • Case study demonstrating the system efficiency and computational effectiveness of proposed algorithm over previous methods. [ABSTRACT FROM AUTHOR]

• A novel model of loop metro service driven by stochastic demand of general distribution. • A Markov decision process for optimal schedule with circulation of limited rolling stock. • An actor-critic deep reinforcement learning framework with an off-policy training algorithm. • A case study demonstrating the efficiency and robustness of the proposed control system. This paper presents a novel actor-critic deep reinforcement learning approach for metro train scheduling with circulation of limited rolling stock. The scheduling problem is modeled as a Markov decision process driven by stochastic passenger demand. As in most dynamic optimization problems, the complexity of the scheduling process grows exponentially with the amount of states, decisions, and uncertainties involved. This study aims to address this 'curses of dimensionality' issue by adopting an actor-critic deep reinforcement learning solution framework. The framework simplifies the evaluation and searching process for potential optimal solutions by parameterizing the original state and decision spaces with the use of artificial neural networks. A deep deterministic policy gradient algorithm is developed for training the artificial neural networks via simulated system transitions before the actor-critic agent can be applied for online schedule control. The proposed approach is tested with a real-world scenario configured with data collected from the Victoria Line of London Underground, UK. Experiment results illustrate the advantages of the proposed method over a range of established meta-heuristics in terms of computing time, system efficiency, and robustness under different stochastic environments. This study innovates urban transit operations with state-of-the-art computer science and dynamic optimization techniques. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of incorporating spatiotemporal wind data on the performance of wind forecasting neural networks. While previous studies have shown that including spatial data enhances the accuracy of such models, limited research has explored the impact of different spatial and temporal scales of input wind data on the learnability of neural network models. In this study, convolutional neural networks (CNNs) are employed and trained using various scales of spatiotemporal wind data. The research demonstrates that using spatiotemporally correlated data from the surrounding area and past time steps for training a CNN favorably affects the predictive performance of the model. The study proposes correlation analyses, including autocorrelation and Pearson correlation analyses, to unveil the influence of spatiotemporal wind characteristics on the predictive performance of different CNN models. The spatiotemporal correlations and performances of CNN models are investigated in three regions: Korea, the USA, and the UK. The findings reveal that regions with smaller deviations of autocorrelation coefficients (ACC) are more favorable for CNNs to learn the regional and seasonal wind characteristics. Specifically, the regions of Korea, the USA, and the UK exhibit maximum standard deviations of ACCs of 0.100, 0.043, and 0.023, respectively. The CNNs wind prediction performances follow the reverse order of the regions: UK, USA, and Korea. This highlights the significant impact of regional and seasonal wind conditions on the performance of the prediction models. [Display omitted] • 3D CNN-based wind velocity prediction. • Estimation of CNN learnability through spatiotemporal wind correlation analysis. • Discovery of influence of local geometric and seasonal wind on CNN prediction. • The proposed correlation analysis can aid in selecting yaw-control wind farm sites. [ABSTRACT FROM AUTHOR]

• An algorithm to automatically detect motion artefacts in cardiac MR short axis images. • Use of k-space corruption to generate realistic motion artefacts to address data imbalance. • Training of a spatio-temporal convolutional neural network using curriculum learning. • Detailed comparison against a range of machine learning methods. • An area under ROC curve of 0.89 is achieved. Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89. [ABSTRACT FROM AUTHOR]

• A novel deep CNN architecture is proposed for jointly learning the expert consensus label and the annotator's label. The proposed architecture (Fig. 1) consists of two coupled CNNs where one estimates the expert consensus label probabilities and the other models the characteristics of individual annotators (e.g., tendency to over-segmentation, mix-up between different classes, etc) by estimating the pixel-wise confusion matrices (CMs) on a per image basis. Unlike STAPLE [25] and its variants, our method models, and disentangles with deep neural networks, the complex mappings from the input images to the annotator behaviours and to the expert consensus label. • The parameters of our CNNs are "global variables" that are optimised across different image samples; this enables the model to disentangle robustly the annotators' mistakes and the expert consensus label based on correlations between similar image samples, even when the number of available annotations is small per image (e.g., a single annotation per image). In contrast, this would not be possible with STAPLE [25] and its variants [5,8] where the annotators' parameters are estimated on every target image separately. • This paper extends the preliminary version of our method presented at the NeurIPS Thirty-fourth Annual Conference on Neural Information Processing Systems [30] , by extensively evaluating our model on a new created real-world multiple sclerosis lesion dataset (QSMSC at UCL: Queen Square Multiple Sclerosis Center at UCL, UK). This dataset is generated with manual segmentations from 4 different annotators (3 radiologists with different level skills and 1 expert to generate the expert consensus label). Additionally, we presented a comprehensive discussion about our model's potential applications (e.g., estimate annotator's quality and annotation's quality), the future works we are going to explore, and the potential limitations of our model. [Display omitted] Supervised machine learning methods have been widely developed for segmentation tasks in recent years. However, the quality of labels has high impact on the predictive performance of these algorithms. This issue is particularly acute in the medical image domain, where both the cost of annotation and the inter-observer variability are high. Different human experts contribute estimates of the "actual" segmentation labels in a typical label acquisition process, influenced by their personal biases and competency levels. The performance of automatic segmentation algorithms is limited when these noisy labels are used as the expert consensus label. In this work, we use two coupled CNNs to jointly learn, from purely noisy observations alone, the reliability of individual annotators and the expert consensus label distributions. The separation of the two is achieved by maximally describing the annotator's "unreliable behavior" (we call it "maximally unreliable") while achieving high fidelity with the noisy training data. We first create a toy segmentation dataset using MNIST and investigate the properties of the proposed algorithm. We then use three public medical imaging segmentation datasets to demonstrate our method's efficacy, including both simulated (where necessary) and real-world annotations: 1) ISBI2015 (multiple-sclerosis lesions); 2) BraTS (brain tumors); 3) LIDC-IDRI (lung abnormalities). Finally, we create a real-world multiple sclerosis lesion dataset (QSMSC at UCL: Queen Square Multiple Sclerosis Center at UCL, UK) with manual segmentations from 4 different annotators (3 radiologists with different level skills and 1 expert to generate the expert consensus label). In all datasets, our method consistently outperforms competing methods and relevant baselines, especially when the number of annotations is small and the amount of disagreement is large. The studies also reveal that the system is capable of capturing the complicated spatial characteristics of annotators' mistakes. [ABSTRACT FROM AUTHOR]

Multiphase flowmeters are important to monitor oil wells, as they allow operators to obtain a real-time estimate of the production. However, due to its high installation cost, uncertainty, and reading errors, the oil and gas industry started to invest in virtual meters, mathematical models that can obtain the oil and gas flow forecast using the instrumentation already available in the well, such as pressure and temperature sensors. The purpose of this article is to develop a virtual multiphase flow predictor using artificial intelligence models such as Long Short-Term Memory (LSTM), Gate Recurrent Units (GRU), Multi-Layer Perceptron (MLP) neural networks and deep learning. As input data, this paper uses a public database available on the Oil and Gas Authority (UK) to train the models and demonstrate the possibility of predicting multiphase flow with a reasonable error margin, proving that the method is efficient in estimating well production rates. • In the oil industry, it is important to measure the production from each well. • A Virtual Meter is a low-cost alternative. It can measure multiphase flow without any additional equipment. • This paper proposes a Virtual Meter using neural networks such as MLP, LSTM, GRU and deep learning combinations. • Prediction performance was evaluated using the Oil and Gas Authority (OGA) public database and it shows promising results. • Experiments using a combination of multiple layers shows this type of prediction takes advantage of deep learning techniques. [ABSTRACT FROM AUTHOR]

The urban morphology is characterised by self-organisation where interactions of multiple agents produce emerging patterns on the urban form. Port-urban relationship added to the complexity of port cities' urban form. Most urban cellular automata (CA) models simulate land-use evolution through transition rules representing multi-factored local interactions. However, calibration of CA-based urban land use and transport interaction (LUTI) models often utilise manual methods due to complexity of the process. This limits insights on urban interactions to a few explored settlements and prevents applications for planning and assessment of transport policies in other contexts. This paper, therefore, addresses three main points. The paper (i) demonstrates an improved method for the calibration of CA-based LUTI models, (ii) contributes to a better understanding of the urban dynamics in port city systems by quantifying generalizable interactions from a wide range of port-urban settlements, and (iii) illustrates how the use of these interactions in a simulation model can allow long-term impact predictions of planning interventions. These were done by formulating a model in a similar structure as a neural network model to enable automatic calibration using an application of the gradient-descent algorithm. The model was then used to quantify the dynamics between land-use, geographic, and transport factors in 46 port-based and 10 non-port settlements across Great Britain, thus enabling cross-sectional analysis. Cluster analysis of the calibrated interactions in the study areas was conducted to examine the variations of these interactions. This produced two main groups. In the first group, consisting larger settlements, connections between ports and other urban activities were weaker than in the second group which consisted of smaller port-settlements. Overall, the findings of the research are consistent with existing evidence in the port-cities literature but go further in quantifying the interaction between urban agents within port-urban systems of various sizes and types. These quantified interactions will enable planners to better predict the longer-term consequences of their interventions. • This paper improved calibration process of urban model using optimization method from machine learning • The improved process allowed examination of wide range of port cities to counter the current trend of case-specific studies • Quantified interactions from wide range of port cities provide insights of the different land use behaviour in different types of port cities • Port-urban interactions in smaller urban areas with port are stronger thn in most other port cities • Interactions quantified in the study can be used in simulation to predict future port city urban form [ABSTRACT FROM AUTHOR]

The GRANIT11GRANIT is a registered trademark, and stands for Ground Anchorage Integrity Testing. system is a non-destructive integrity testing method for ground anchorages. It has won two major UK Awards—the Design Council Millennium Product Status in 1999 and the John Logie Baird Award in 1997. It makes use of novel artificial intelligence techniques in order to learn the complicated relationship that exists between an anchorage and its frequency response to an impulse. The GRANIT system has a worldwide patent and is currently licensed to AMEC plc.It is widely recognised that non-destructive testing methods for ground anchorages need to be developed as a high priority [A National Agenda for Long-term and Fundamental Research for Civil Engineering in the United Kingdom (1992)], with only between 1 and 5% of anchorages currently being monitored in service [British Standard Code of Practice for Ground Anchorages (8081) (1989)] using currently available techniques. The GRANIT system is a solution for this requirement, and this paper describes how the use of artificial intelligence techniques enabled, for the first time, the cross-anchorage diagnosis of ground anchorages, where data taken from one anchorage was used to train a neural network, which was then used to diagnose the condition of an adjacent anchorage.The results presented in this paper describe the training of a neural network on data taken from a bolt anchorage, and the diagnosis, using this neural network, of further test data taken from the same anchorage. Data taken from an adjacent anchorage of similar construction is also presented to the neural network, and the cross-anchorage diagnosis of the load level of the second anchorage is achieved. [Copyright &y& Elsevier]

• BP-ANN was applied to model the factors of older female and male driver accidents. • Models have greater accuracy and define the proportional contribution of errors. • 95,092 accident records in West Midlands during 2006-2016 were analysed. • Vital causes are journey purpose, lighting, pedestrian crossing & extreme weather. • Decline in abilities & cut-off of public transport make older people reliant on car. Older people are vulnerable road users with higher rate of casualties in traffic accidents. The commonly cited causes of accidents for older people are poor attention and decision making at critical locations of road, poor visibility in extreme weather, poor road surface condition and unpredictability of other road users, particularly young drivers. Female drivers are often labelled as being precarious drivers and having higher accident risk comparing to male drivers. This paper applies Backpropagation - Artificial Neural Network (BP-ANN) with a Generalized Delta Rule (GDR) learning algorithm to model the factors affecting traffic accidents of both older female and male drivers. The BP-ANN can construct the causation model of traffic accidents with greater accuracy and define the proportion of errors contributed by each factor to traffic accidents. This paper studies a total of 95,092 accident records in West Midlands of the United Kingdom during the period of 2006 to 2016. This paper determines journey purpose, lighting condition, pedestrian crossing with physical interventions, complex roadway geometry, extreme weather and time severity as the most significant factors of older driver accidents. The accident risk of older drivers can be improved by providing accessible routes, affordable, reliable and convenient public transport, timely warning of unexpected situations and changes in roadway geometry; increasing use of assistive technology in cars, driverless cars and encouraging active transports into sociable activities. The findings help the transport authorities and city councils to develop strategies and measures promoting public and active transports to ensuring the safety of older drivers. [ABSTRACT FROM AUTHOR]