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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]

To optimize the operation of his wind farm, the farm manager needs to make precise diagnostic decisions for scheduling efficiently the maintenance actions. To assist him in this task, this paper proposes an approach aimed at designing a hybrid diagnoser model for two main goals. The first one is to detect anomalies at an early stage, and the second one is to provide specific deductions or explanations about the potential cause of the problem. In this way, the developed system can help the maintenance manager understand why a particular decision needs to be made. To do this, an original approach coupling the artificial intelligence technique with a discrete event system is proposed to diagnose, identify, and explain a probable source of a problem. Thus, the proposed method uses a hybrid approach consisting of two model blocks. The first block consists of autoencoder models to extract feature representation to diagnose the health state of the system. The second one is a discrete event-based model to create and visualize rule-based anomaly alerts and triggers to provide plausible explanations to the operator to improve his task. Then, this work introduces a methodology to jointly train these two models and learn all parameters of the hybrid diagnoser. It is shown how the information extracted from the neural network model is used to automatically construct the event-based model to explain the occurrence of an anomaly. The proposed system achieved significant results in explaining and detecting early five types of anomalies in wind turbine systems with accuracy up to 80%. The results demonstrate that the approach can deliver performance gains of up to 20% compared to standard techniques such as Long short-term memory and Random cut forest. • Anomalies detection and explanation in wind turbine systems. • Hybrid learning of an autoencoder and event-based model. • Computation of a set of rules for identifying the root causes of anomalies. • Explainable predictive maintenance for improving decision-making processes. [ABSTRACT FROM AUTHOR]

Recently, facial expression recognition (FER) methods have achieved significant progress. However, FER is still challenged by factors such as uneven illumination and low-quality expression images. Exploring the potential of facial expression features to achieve robust FER is particularly important. Inspired by the Transformer's excellent performance in modeling long-range dependencies and in vision tasks, this paper proposes a Transformer Block Enhancement Module (TBEM) for enhancing the feature representation of facial expressions. The proposed module contains a Channel Enhancement (CE) block and a Spatial Enhancement (SE) block. The CE block can adaptively enhance the expression features on the channel dimension by effectively leveraging the channel dependency information, while the SE block enhances the expression features on the spatial dimension by integrating the spatial dependency information. TBEM can output a more robust expression representation by combining CE and SE. The proposed artificial intelligence learning module greatly improves the recognition accuracy of FER engineering tasks. To further illustrate the application of TBEM in real-world FER engineering, three engineering problems are used for verification. Extensive experiments demonstrate that the proposed method improves FER performance by focusing on more accurate decisional features and can be easily embedded into regular convolutional neural network models to help them improve the accuracy on FER tasks by about 2.64%–3.03%. The results show the proposed method achieves accuracies of 90.57% on FERPlus, 89.41% on RAFDB basic and 68.43% on RAFDB compound, respectively. The proposed method also provides a meaningful reference for further research on applying Transformer to other tasks. [ABSTRACT FROM AUTHOR]

In recent years, the use of artificial intelligence-based methods in engineering problems has been expanded. In the current study, the method of artificial neural networks (ANN) has been employed to predict the ultimate strength of bolted shear connectors in cold-formed steel (CFS) composite beams. For this purpose, multilayer perceptron (MLP) networks with a hidden layer were used. Three parameters affecting the performance of these networks, including the training algorithm, the activation function in the hidden layer, and the number of neurons in the hidden layer, were examined and the most accurate network was selected. The input and target data for training the network were provided by conducting an extensive numerical study on the behavior of bolted shear connectors in CFS composite beams. Consequently, using ABAQUS software, finite element (FE) models validated with experimental results were first developed. Then, 216 models with different characteristics were analyzed and a reliable database was provided for the development of neural networks. Moreover, in order to prove the high accuracy of the ANN method, the stepwise regression (SR) method was also developed as one of the powerful regression-based methods, and the performances of these two methods were compared. Finally, the most important purpose of this study is to propose an accurate ANN-based formulation in order to predict the ultimate strength of bolted shear connectors in CFS composite beams. Due to the fact that so far no relationship has been proposed to predict the resistance of shear connectors in CFS composite beams, the formula presented in this paper can be helpful in the design process of this type of beams. [ABSTRACT FROM AUTHOR]

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the 'system of systems' approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems. • Literature on the use of AI in infrastructure sectors is reviewed. • Types of AI, trends across sectors and purposes within infrastructure are identified. • Findings have been evaluated using an analysis framework. • The future of AI and the 'Survey PaperInternet of Things' in infrastructure are discussed. [ABSTRACT FROM AUTHOR]

Text understanding and reasoning are among the core areas of artificial intelligence. Even a total solution to automatic text understanding and reasoning is still beyond the current techniques, thus it is time to build the stepping stones to solve a more straightforward problem set like question-answering (QA) without ambiguous utterances in the contexts or questions. The reported state-of-the-art approaches to this kind of problem are nearly all connectionist models based on neural networks. Significant progress has even been made in this direction. It is still hard for the pure connectionist models to handle logical reasoning. They generally suffer from the longstanding drawback of poor explainability and sensitivity to data noise and distribution. In this paper, we propose a complementary symbolic approach, GMR (Graph Matching based Reasoner) to QA — it automatically generates reasoning rules in the form of graphs from the training set and uses the generated rules to infer answers to the questions in the test set via graph matching. By employing this symbolic approach, 20 tasks in bAbI are solved with an average accuracy of 99.38%, and it outperforms the state-of-the-art for a real-life QA dataset WikiTableQuestions. After analyzing the accuracy of the basic evaluation indicators, we studied the generalization ability of the model in the paper, including the anti-noise ability, the convergence of the model, the stability of the model, the complexity of the algorithm, and the uncertainty of the parameters. Through comprehensive analysis and comparison, our model is stronger than the neural network model regarding anti-noise interference. Compared with the neural network model, our model performs very well in multi-tasking, and the stability of the model is quite high. The diversity of tasks did not reduce the stability of the model. We have conducted a comprehensive analysis and comparison of the parameter uncertainties. Our model can optimally select parameter configurations and will not cause a sharp drop in performance due to the parameter uncertainties. Finally, we describe the complexity of the GMR method and the optimal configuration of its parameters. [ABSTRACT FROM AUTHOR]

Multi-step time-series prediction is an essential supportive step for decision-makers in several industrial areas. Artificial intelligence techniques, which use a neural network component in various forms, have recently frequently been used to accomplish this step. However, the complexity of the neural network structure still stands up as a critical problem against prediction accuracy. In this paper, a method inspired by the proportional–integral–derivative (PID) control approach is investigated to enhance the performance of neural network models used for multi-step ahead prediction of periodic time-series information while maintaining a negligible impact on the complexity of the system. The PID-based method is applied to the predicted value at each time step to bring that value closer to the real value. The water demand forecasting problem is considered as a case study, where two deep neural network models from the literature are used to prove the effectiveness of the proposed boosting method. Furthermore, to prove the applicability of this PID-based booster to other types of periodic time-series prediction problems, it is applied to enhance the accuracy of a neural network model used for multi-step forecasting of hourly energy consumption. The comparison between the results of the original prediction models and the results after using the proposed technique demonstrates the superiority of the proposed method in terms of prediction accuracy and system complexity. [ABSTRACT FROM AUTHOR]